nirantk/triplets · Datasets at Hugging Face

query stringlengths 1 13.4k	pos stringlengths 1 61k	neg stringlengths 1 63.9k	query_lang stringclasses 147 values	__index_level_0__ int64 0 3.11M
Bayesian analysis with consideration of data uncertainty in a specific scenario	Abstract In this paper data uncertainty in a specific scenario is modeled with Berkson and Classical errors. With consideration of uncertainty three methods within Bayesian framework are presented to update the failure probability with Beta-Binomial model. It shows that the three methods have their posteriors in the same form of weighted Beta distributions, but the weights are different for each method. Approximation to the mixed posteriors has been proposed and demonstrated by computation results. Moreover, comparison and illustration of the three methods are made based on case study and analytical analysis, which suggest that the LO method with Classical error model be more appropriate in similar applications.	According to the principle and algorithm of BP neural network,the structure of vibrating sieve can be determined.The BP neural network model for fault diagnosing is obtained by taking the characteristic variables of operating state of vibrating sieve as the input of neural network and using the Matlab neural network toolbox for the network training.The experimental result shows that the method can accurately diagnose the fault of vibrating sieve.	eng_Latn	34,000
Ergonomics Solutions in Dental Practice	Increased modernization and immense use of technology in dental practice has exposed dentists to high risk of poor postural habits thus leading to high risk of musculoskeletal disorders like low back pain, tennis elbow and carpel tunnel syndrome etc. This review paper highlights the importance of ergonomic solutions in dental practice.	Abstract In this paper, identification of the joint probability dentisity function (PDF) from missing data is considered. The model of PDF is Gaussian mixture. It is well known that the expectation-maximization (EM) algorithm is useful for the identification of Gaussian mixture. Here it is extended to the case of missing elements of the observations. It will be shown that, after identifying the PDF model, it is easy to estimate the missing elements as well as the system output variable.	eng_Latn	34,001
Estimation of Reliability Parameters Under Incomplete Primary Information	We consider the procedure for small-sample estimation of reliability parameters. The main shortcomings of the classical methods and the Bayesian approach are analyzed. Models that find robust Bayesian estimates are proposed. The sensitivity of the Bayesian estimates to the choice of the prior distribution functions is investigated using models that find upper and lower bounds. The proposed models reduce to optimization problems in the space of distribution functions.	The present study develops a non-parametric Bayesian network (NPBN) model to predict the corrosion depth on buried pipelines using the pipeline age and local soil properties. The dependence structu...	eng_Latn	34,002
Iterative Data Completion for Limited Angle Tomography using Filtered Backprojection	When the range of projection angles is limited, tomographic reconstruction suffers from artifacts caused by incomplete data. One can consider a data completion technique, which estimates projection data at unobserved angles using a prior knowledge or mathematical exploration, but the result is often not improved; the improvement by the data completion often undermined by the artifacts by inaccurate estimation, In this paper, we propose an iterative method, which computes projection data at unobserved angles by using the current estimate on the image, links the computed projection data to the observed ones by using the consistence condition of Radon transform, and reconstruct the next estimate on the image by filtered backprojection. The proposed method does not require a prior knowledge on the image, and has much faster approximation rate than the expectation maximization method. The performance of the proposed method was tested through several simulation studies.	We present a scalable approach to perform- ::: ing approximate fully Bayesian inference in ::: generic state space models. The proposed ::: method is an alternative to particle MCMC ::: that provides fully Bayesian inference of both ::: the dynamic latent states and the static pa- ::: rameters of the model. We build up on re- ::: cent advances in computational statistics that ::: combine variational methods with sequential ::: Monte Carlo sampling and we demonstrate ::: the advantages of performing full Bayesian in- ::: ference over the static parameters rather than ::: just performing variational EM approxima- ::: tions. We illustrate how our approach enables ::: scalable inference in multivariate stochastic ::: volatility models and self-exciting point pro- ::: cess models that allow for ﬂexible dynamics ::: in the latent intensity function.	eng_Latn	34,003
Paratesticular embyronal rhabdomyosarcoma in an adolescent: A rare case report	Embyronal rhabdomyosarcoma (RMS) accounts for approximately 49% of all RMS. After head and neck, this tumor is most commonly found in genitourinary region, which includes paratesticular RMS. Paratesticular RMS is rare constituting 4-7% of all RMS in children and young adults. It has been regarded as highly malignant tumor with frequent recurrence. The management protocol is of multimodal approach of surgery, chemo, and radiotherapy. We herein report a case of left paratesticular RMS in an 18-year-old male, which posed a diagnostic dilemma clinically and by imaging. Histopathology with added immunohistochemistry brought out the confirmatory diagnosis. The patient was successfully treated and on follow-up is disease free until date.	ABSTRACTLongitudinal data are commonly modeled with the normal mixed-effects models. Most modeling methods are based on traditional mean regression, which results in non robust estimation when suffering extreme values or outliers. Median regression is also not a best choice to estimation especially for non normal errors. Compared to conventional modeling methods, composite quantile regression can provide robust estimation results even for non normal errors. In this paper, based on a so-called pseudo composite asymmetric Laplace distribution (PCALD), we develop a Bayesian treatment to composite quantile regression for mixed-effects models. Furthermore, with the location-scale mixture representation of the PCALD, we establish a Bayesian hierarchical model and achieve the posterior inference of all unknown parameters and latent variables using Markov Chain Monte Carlo (MCMC) method. Finally, this newly developed procedure is illustrated by some Monte Carlo simulations and a case analysis of HIV/AIDS clinical...	eng_Latn	34,004
the time has come bayesian methods for data analysis in the organizational sciences .	What to believe : Bayesian methods for data analysis	Corticofugal modulation of the midbrain frequency map in the bat auditory system	eng_Latn	34,005
Neural Architecture Search with Bayesian Optimisation and Optimal Transport	Practical Bayesian Optimization of Machine Learning Algorithms	Power electronics and future marine electrical systems	eng_Latn	34,006
Bayesian Just-So Stories in Psychology and Neuroscience	how persuasive is a good fit ? a comment on theory testing .	Single Image Layer Separation Using Relative Smoothness	eng_Latn	34,007
Survey on data science with population-based algorithms	sequential monte carlo samplers .	an adaptive level set approach for incompressible two - phase flows 1 .	eng_Latn	34,008
Group-Based Trajectory Modeling in Clinical Research	Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study	Design and development of IGBT resonant inverters for domestic induction heating applications	eng_Latn	34,009
Object-oriented Bayesian networks for detection of lane change maneuvers	Object-Oriented Bayesian Networks	different emotional reactions to different groups : a sociofunctional threat - based approach to " prejudice " .	eng_Latn	34,010
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search	Derivative-free optimization: a review of algorithms and comparison of software implementations	Measuring isotropic subsurface light transport	eng_Latn	34,011
Describing Visual Scenes using Transformed Dirichlet Processes	A Bayesian hierarchical model for learning natural scene categories	Effects of Brief and Sham Mindfulness Meditation on Mood and Cardiovascular Variables	eng_Latn	34,012
Selective Inference for Group-Sparse Linear Models	Model selection and estimation in regression with grouped variables	Difficulties in Simulating the Internet	eng_Latn	34,013
Spiking Neural Networks: Principles and Challenges	Bayesian inference with probabilistic population codes	A gradient descent rule for spiking neurons emitting multiple spikes	eng_Latn	34,014
Optimal Proposal Distributions and Adaptive MCMC	sequential monte carlo samplers .	Prp40 and early events in splice site definition.	eng_Latn	34,015
Coverage directed test generation for functional verification using Bayesian networks	Learning Dynamic Bayesian Networks	An Enhanced Grid Current Compensator for Grid-Connected Distributed Generation Under Nonlinear Loads and Grid Voltage Distortions	eng_Latn	34,016
An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions	The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo	Genomic selection for wheat traits and trait stability	eng_Latn	34,017
Asymptotically Exact, Embarrassingly Parallel MCMC	Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring	Protective activity of ketoprofen lysine salt against the pulmonary effects induced by bradykinin in guinea-pigs	eng_Latn	34,018
Decision support system for the glaucoma using Gabor transformation	Learning Bayesian networks: The combination of knowledge and statistical data	Augmentation of the Columella-Labial Angle to Prevent the ``Smiling Deformity'' in Rhinoplasty	eng_Latn	34,019
On Tikhonov Regularization, Bias and Variance in Nonlinear System Identification	bayesian methods for adaptive models thesis by second edition bayesian methods for adaptive models .	Based on the Texture Analysis to Inspect the Tread Worn Status on the Tire	eng_Latn	34,020
Copula bayesian networks	An Introduction to Variational Methods for Graphical Models	Intelligence Measure of Cognitive Radios with Learning Capabilities	eng_Latn	34,021
Learning Symbolic Models of Stochastic Domains	STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving	The use of CORDIC in software defined radios: a tutorial	eng_Latn	34,022
Nested Sequential Monte Carlo Methods	Monte Carlo smoothing for non-linear time series	On sequential Monte Carlo sampling methods for Bayesian filtering	eng_Latn	34,023
Model selection in ecology and evolution	Bayesian Model Selection and Model Averaging	Design and Analysis of Swapped Port Coupler and Its Application in a Miniaturized Butler Matrix	eng_Latn	34,024
Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach	A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning	Design of water cooled electric motors using CFD and thermography techniques	eng_Latn	34,025
Sparse nonparametric density estimation in high dimensions using the rodeo	Bayesian Density Estimation and Inference Using Mixtures	Modeling and control design of Magnetic levitation system	eng_Latn	34,026
On a New Improvement-Based Acquisition Function for Bayesian Optimization	Adaptive simulated annealing (ASA): Lessons learned	Linear and quadratic time-frequency signal representations	eng_Latn	34,027
A Bayesian network framework for reject inference	The Foundations of Cost-Sensitive Learning	Trauma-and Attachment-Informed Sensory Integration Assessment and Intervention n	eng_Latn	34,028
On the half-Cauchy prior for a global scale parameter	Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction	The enactment of socio-technical transition pathways: A reformulated typology and a comparative multi-level analysis of the German and UK low-carbon electricity transitions (1990–2014)	eng_Latn	34,029
Monte Carlo methods for massively parallel computers	Monte Carlo Methods in Classical Statistical Physics	LTE-based passive device-free crowd density estimation	eng_Latn	34,030
Probabilistic inference as a model of planned behavior	Anytime Point-Based Approximations for Large POMDPs	Health Working Papers No . 63 Value in Pharmaceutical Pricing	eng_Latn	34,031
Bayesian Model Averaging: A Tutorial	Bayesian Variable Selection	Model of Differentiation between Normal and Abnormal Heart Sounds in Using the Discrete Wavelet Transform	kor_Hang	34,032
Bayesian Ascent: A Data-Driven Optimization Scheme for Real-Time Control With Application to Wind Farm Power Maximization	Derivative-free optimization: a review of algorithms and comparison of software implementations	A navigation model for exploring scientific workflow provenance graphs	kor_Hang	34,033
A Novel Experimental Platform for In-Vessel Multi-Chemical Molecular Communications	Molecular MIMO: From Theory to Prototype	Subjective bayesian methods for rule-based inference systems	eng_Latn	34,034
Assessing the Value of the Threshold Parameter in the Weibull Distribution Using Bayes Paradigm	The Weibull distribution represents a wide variety of situations. Usually, the distribution is considered as a two-parameter family with a scale, and a shape parameter. If, however, the given data reflect additional information in the form of a minimum guarantee, a positive value away from zero, it is better to go for a three-parameter model with the additional parameter known as the threshold. The threshold parameter is often very important, but increases the complexity of the model. Arbitrarily going for the three-parameter form is not advisable unless it is really required by the data. This article attempts to make a simulation-based Bayesian study for checking if the threshold parameter can be taken to be zero or positive in situations representing the two models. We study the compatibility of the models for the given data set. We conduct the posterior simulation in each case using Gibbs sampling.	The main objective of this article is to study dynamic of the ::: three-dimensional Boussinesq equations with the periodic boundary ::: condition.We prove that when the Rayleigh number $R$ crosses the ::: first critical Rayleigh number $R_c$, the Rayleigh-Benard problem ::: bifurcates from the basic state to an global attractor $\Sigma$, which is homeomorphic to $S^3$.	eng_Latn	34,035
Height Reconstruction inHighly Sloped AreaUsingMulti-frequency InSARData	Withtheemerging ofmulti-frequency InSARsystem, extraction of3-Dandvariation information ofearth surface usingmulti-frequency InSARdatahasdrawnmore attention. Methodsbasedon Maximum-Likelihood Estimation (MLE),combining withsomeconventional phaseunwrapping algorithm canbeusedtoachieve this purpose. Inthispaper, MLE integrated withweighted multigrid phase-unwrapping method forhigh-sloped terrain profile reconstruction ispresented andstudied indetail. The performance ofthis methodisalsoanalyzed. Experiment result using simulated InSARdatashowsthat themethod canbe usedtoreconstruct high-sloped terrain with relatively high accuracy. IndexTerms-InSAR,height reconstruction, MLE, phase-unwrapping	AbstractInterval-censored survival data arise often in medical applications and clinical trials [Wang L, Sun J, Tong X. Regression analyis of case II interval-censored failure time data with the additive hazards model. Statistica Sinica. 2010;20:1709–1723]. However, most of existing interval-censored survival analysis techniques suffer from challenges such as heavy computational cost or non-proportionality of hazard rates due to complicated data structure [Wang L, Lin X. A Bayesian approach for analyzing case 2 interval-censored data under the semiparametric proportional odds model. Statistics & Probability Letters. 2011;81:876–883; Banerjee T, Chen M-H, Dey DK, et al. Bayesian analysis of generalized odds-rate hazards models for survival data. Lifetime Data Analysis. 2007;13:241–260]. To address these challenges, in this paper, we introduce a flexible Bayesian non-parametric procedure for the estimation of the odds under interval censoring, case II. We use Bernstein polynomials to introduce a prior for m...	eng_Latn	34,036
Analysis of Wine Bouquet Components Using Headspace Solid‐Phase Microextraction‐Capillary Gas Chromatography	Headspace solid-phase microextraction (HS/SPME) was studied and optimized for the capillary gas chromatographic (CGC) analysis of wine aroma compounds. The results were compared with those obtained using the direct sampling mode (DI/SPME) and using liquid/liquid extraction with Kaltron. The aromatic patterns obtained by HS/SPME-CGC were applied to the chemometric classification of wine varieties. The HS/SPME-CGC standard additional method is an appropriate technique for the quantitative analysis of volatile wine aroma compounds.	Two-choice response times are a common type of data, and much research has been devoted to the development of process models for such data. However, the practical application of these models is notoriously complicated, and flexible methods are largely nonexistent. We combine a popular model for choice response times—the Wiener diffusion process—with techniques from psychometrics in order to construct a hierarchical diffusion model. Chief among these techniques is the application of random effects, with which we allow for unexplained variability among participants, items, or other experimental units. These techniques lead to a modeling framework that is highly flexible and easy to work with. Among the many novel models this statistical framework provides are a multilevel diffusion model, regression diffusion models, and a large family of explanatory diffusion models. We provide examples and the necessary computer code.	kor_Hang	34,037
Broadening of the absorption spectrum in a p-type InGaAs - InAlAs coupled quantum well	The intervalence subband absorption of normally incident infrared radiation in a p-type InGaAs - InAlAs coupled quantum well (CQW) is theoretically investigated by the multiband effective mass formalism. We calculate valence subband structures, intervalence subband transition matrix elements and the absorption coefficient spectrum in the CQW which consists of a wider well, a thinner well and a barrier between. We demonstrate that the absorption coefficient profile can be tailored to suit one's need by careful control of the flexible design parameters given to the valence band CQW structure. For the application to an infrared detector with a flat absorptivity over a wider wavelength range than the conventional detectors, the CQW is designed to have broadened absorption spectrum.	ABSTRACTLongitudinal data are commonly modeled with the normal mixed-effects models. Most modeling methods are based on traditional mean regression, which results in non robust estimation when suffering extreme values or outliers. Median regression is also not a best choice to estimation especially for non normal errors. Compared to conventional modeling methods, composite quantile regression can provide robust estimation results even for non normal errors. In this paper, based on a so-called pseudo composite asymmetric Laplace distribution (PCALD), we develop a Bayesian treatment to composite quantile regression for mixed-effects models. Furthermore, with the location-scale mixture representation of the PCALD, we establish a Bayesian hierarchical model and achieve the posterior inference of all unknown parameters and latent variables using Markov Chain Monte Carlo (MCMC) method. Finally, this newly developed procedure is illustrated by some Monte Carlo simulations and a case analysis of HIV/AIDS clinical...	eng_Latn	34,038
Bayesian Hierarchical Model for Identifying Changes in Gene Expression from Microarray Experiments	Recent developments in microarrays technology enable researchers to study simultaneously the expression of thousands of genes from one cell line or tissue sample. This new technology is often used to assess changes in mRNA expression upon a specified transfection for a cell line in order to identify target genes. For such experiments, the range of differential expression is moderate, and teasing out the modified genes is challenging and calls for detailed modeling. The aim of this paper is to propose a methodological framework for studies that investigate differential gene expression through microarrays technology that is based on a fully Bayesian mixture approach (Richardson and Green, 1997). A case study that investigated those genes that were differentially expressed in two cell lines (normal and modified by a gene transfection) is provided to illustrate the performance and usefulness of this approach.	It is the purpose of this paper to introduce a novel estimator for the extremal index of an instantaneous function {f(Xn)}n of a regenerative Harris Markov chain X, based on the renewal properties of the latter. The estimate proposed may be viewed as a "regenerative version" of the runs estimator, insofar as it measures the clustering tendency of high threshold exceedances within regeneration cycles. Strong consistency of this estimator is established under mild stochastic stability assumptions and a simulation result is displayed in the case when the underlying chain is the waiting process related to a simple M/M/1 queue.	eng_Latn	34,039
Statistical analysis of mixtures underlying probability of ruin	If the hypothesis on exponentially distributed claims in a risk (or surplus) model is untenable then, in many cases, the assumption that they are mixtures of two (or more) exponentials is a suitable substitute. In the first part of the paper tests of homogeneity for exponentially distributed claims are discussed and their properties are stated. The statistical properties of parameter estimations for such claims are also mentioned. In the second part the classical Cramer-Lundberg ruin model is discussed when claims are distributed as mixtures of exponentials. Our attention is focussed primarily on assesment of accuracy of approximations obtained. Then our results are compared to those already known.	Study classical general mathematics model bag fetch ball,arrange in an order,put ball not to enter case analytical method of problem,utilize the these problems analytical method to solve some classical and general probability and calculate the problem.	eng_Latn	34,040
Global Uncertainty and Sensitivity Analysis and Neighbourhoods	We shall briefly review recent progress in Global Quantitative Uncertainty and Sensitivity Analysis (UA/SA) techniques, relating these to multidimensional global calibration approaches of the “Monte Carlo filtering” type. Global quantitative techniques for Sensitivity Analysis (SA), that are based on the decomposition of the variance of the target model output, have received a considerable boost in recent years, due both to more efficient computational strategies and to a widening of their range of applications. Monte Carlo Filtering, and the GLUE (Generalised Likelihood Uncertainty Estimate) approach that derives from it are also promising tools to use in the presence of structural model uncertainty, as in the case of petroleum engineering that was the focus of the ECMI2002 mini-symposium “Advanced Mathematical Tools for Petroleum System Modelling”.	Living in a dwelling that is close to greenspace reduces youngsters’ risk for behaviors associated with neurobehavioral problems. This relationship varies with the type of behavior, the child’s age, and the proximity of the greenspace, according to a study conducted in an ongoing prospective birth cohort.	eng_Latn	34,041
Limited sampling model for the estimation of pharmacokinetic parameters in children.	Summary:A limited sampling model (LSM) is proposed for the first-time assessment of pharmacokinetic parameters (area under the concentration–time curve (AUC), Cmax, and T½) in children after a single oral dose of drug. Three drugs were evaluated in this study. The LSM was developed for each drug fro	This paper presents an extension of Petri net framework with imprecise temporal properties. We use possibility theory to represent imprecise time by time-stamping tokens and assigning durations to firing of the transitions. A method for approximation of an arbitrary temporal distribution with a set of possibilistic intervals is used to introduce the composition operation for two possibilistic temporal distributions. We developed a method to determining an effective enabling time of a transition with incoming tokens with possibilistic distributions. The utility of the proposed theory is illustrated using an example of an automated manufacturing system. The proposed approach is novel and has a broad utility beyond a timed Petri network and its applications.	eng_Latn	34,042
Modeling dichotomous item responses with free-knot splines	Item response theory (IRT) models are a class of generalized mixed effect (GME) models used by psychometricians to describe the response behavior of individuals to a set of categorically scored items. The typical assumptions of IRT are Unidimensionality(U) of the random effect; Conditional (or Local) Independence (CI), the item responses are independent given the random effect; and Monotonicity (M), the probability of a correct response is a non-decreasing function of the random effect. The simple parametric models available in the psychometric literature have proved to be too restrictive in many data sets. Non-parametric regression models are a powerful tool for the estimation of non-linear curves, and have been used in IRT as a flexible way to model the item response function. This paper develops a new method for the non-parametric estimation of item response functions based on reversible-jump Markov Chain Monte Carlo, and demonstrates the practicality of the method by examining two data sets.	We present a connectionist model designed for supervised learning of associated patterns, which is based on Kohonen’s self-organizing feature maps. While learning, the classification is performed on both inputs and desired associated ouputs. The learned weights are then used in exploitation phase to associate an input vector with an output one.	eng_Latn	34,043
Maximum likelihood estimation in hazard rate models with a change-point	The problem of estimation of parameters in hazard rate models with a change-point is considered. An interesting feature of this problem is that the likelihood function is unbounded. A maximum likelihood estimator of the change-point subject to a natural constraint is proposed, which is shown to be consistent.The limiting distributions are also derived.	This article advances a proposal that increases access to justice for valuable lawsuits that are currently discouraged by litigation costs. Our proposal converts claims with negative expected values into positive expected value claims by implementing a novel system involving flexible conditional multipliers. Our proposal has two components. First, under the proposed system a plaintiff is allowed to select a damage multiplier that determines the amount of damages the plaintiff receives if the litigation is successful. Second, courts select cases for litigation randomly with a probability inverse to the multiplier selected by the plaintiff.	eng_Latn	34,044
Femoral nerve compression syndrome with paresis of the quadriceps muscle caused by radiotherapy of malignant tumours. A report of four cases.	Four patients showed signs of femoral nerve compression with subsequent paresis of the quadriceps muscle, after radiation therapy of malignant tumours. The compression was caused by scar tissue due to radiation treatment of the inguinal region. The first symptom was radiating pain in the front of the thigh and lower leg which appeared 12-16 months after X-ray treatment. A decrease in the strength of quadriceps muscle occurred some months later. In one case the femoral nerve was decompressed, another patient was treated by an intradural phenolglycerin injection and one patient was treated with cortisone and oxiphenbutazone. In these cases the pain decreased considerably, but in one case only the paresis of the quadriceps muscle improved after treatment.	ABSTRACTLongitudinal data are commonly modeled with the normal mixed-effects models. Most modeling methods are based on traditional mean regression, which results in non robust estimation when suffering extreme values or outliers. Median regression is also not a best choice to estimation especially for non normal errors. Compared to conventional modeling methods, composite quantile regression can provide robust estimation results even for non normal errors. In this paper, based on a so-called pseudo composite asymmetric Laplace distribution (PCALD), we develop a Bayesian treatment to composite quantile regression for mixed-effects models. Furthermore, with the location-scale mixture representation of the PCALD, we establish a Bayesian hierarchical model and achieve the posterior inference of all unknown parameters and latent variables using Markov Chain Monte Carlo (MCMC) method. Finally, this newly developed procedure is illustrated by some Monte Carlo simulations and a case analysis of HIV/AIDS clinical...	eng_Latn	34,045
Efficient Simulation of Epistatic Interactions in Case-Parent Trios	Statistical approaches to evaluate interactions between single nucleotide polymorphisms (SNPs) and SNP-environment interactions are of great importance in genetic association studies, as susceptibility to complex disease might be related to the interaction of multiple SNPs and/or environmental factors. With these methods under active development, algorithms to simulate genomic data sets are needed to ensure proper type I error control of newly proposed methods and to compare power with existing methods. In this paper we propose an efficient method for a haplotype-based simulation of case-parent trios when the disease risk is thought to depend on possibly higher-order epistatic interactions or gene-environment interactions with binary exposures.	We present a scalable approach to perform- ::: ing approximate fully Bayesian inference in ::: generic state space models. The proposed ::: method is an alternative to particle MCMC ::: that provides fully Bayesian inference of both ::: the dynamic latent states and the static pa- ::: rameters of the model. We build up on re- ::: cent advances in computational statistics that ::: combine variational methods with sequential ::: Monte Carlo sampling and we demonstrate ::: the advantages of performing full Bayesian in- ::: ference over the static parameters rather than ::: just performing variational EM approxima- ::: tions. We illustrate how our approach enables ::: scalable inference in multivariate stochastic ::: volatility models and self-exciting point pro- ::: cess models that allow for ﬂexible dynamics ::: in the latent intensity function.	eng_Latn	34,046
ESPRESS—On efficient bistatic characterization of radar targets	In the modern radar target recognition, the model-based approach offers a flexible and computationally efficient way to characterize targets, since establishing an adequate target signature collection especially with bistatic measurements is impractical. Simulating such an extensive collection is arduous as well. This paper proposes a new method for the bistatic characterization of radar targets and radar response simulation: ESPRESS (Electromagnetic Signature Production from Renders Exploiting Scatterer Sets). We have implemented it entirely with commercial off-the-shelf (COTS) software. Our objective is to give the target a compact description, from which radar response—with arbitrary radar frequency and bandwidth, as well as transmitter and receiver positions—can be simulated efficiently. In this paper, we demonstrate that ESPRESS has the computational speed and adequate accuracy required in model-based radar target recognition.	We present a scalable approach to perform- ::: ing approximate fully Bayesian inference in ::: generic state space models. The proposed ::: method is an alternative to particle MCMC ::: that provides fully Bayesian inference of both ::: the dynamic latent states and the static pa- ::: rameters of the model. We build up on re- ::: cent advances in computational statistics that ::: combine variational methods with sequential ::: Monte Carlo sampling and we demonstrate ::: the advantages of performing full Bayesian in- ::: ference over the static parameters rather than ::: just performing variational EM approxima- ::: tions. We illustrate how our approach enables ::: scalable inference in multivariate stochastic ::: volatility models and self-exciting point pro- ::: cess models that allow for ﬂexible dynamics ::: in the latent intensity function.	eng_Latn	34,047
Probabilistic-Input, Noisy Conjunctive Models for Cognitive Diagnosis	Existing cognitive diagnosis models conceptualize attribute mastery status discretely as either mastery or nonmastery. This study proposes a different conceptualization of attribute mastery as a probabilistic concept, i.e., the probability of mastering a specific attribute for a person, and developing a probabilistic-input, noisy conjunctive (PINC) model, in which the probability of mastering an attribute for a person is a parameter to be estimated from data. And a higher-order version of the PINC model is used to consider the associations among attributes. The results of simulation studies revealed a good parameter recovery for the new models using the Bayesian method. The Examination for the Certificate of Proficiency in English (ECPE) data set was analyzed to illustrate the implications and applications of the proposed models. The results indicated that PINC models had better model-data fit, smaller item parameter estimates, and more refined estimates of attribute mastery.	Abstract Necessary conditions and iterative computational algorithms are obtained for the problem of choosing feedback gains to optimize a linear control system with a quadratic cost function. The results are obtained in abstract terms and cover a wide range of practical problems. A design example is given	eng_Latn	34,048
Infinite mixtures of multivariate Gaussian processes	This paper presents a new model called infinite mixtures of multivariate Gaussian processes, which can be used to learn vector-valued functions and applied to multitask learning. As an extension of the single multivariate Gaussian process, the mixture model has the advantages of modeling multimodal data and alleviating the computationally cubic complexity of the multivariate Gaussian process. A Dirichlet process prior is adopted to allow the (possibly infinite) number of mixture components to be automatically inferred from training data, and Markov chain Monte Carlo sampling techniques are used for parameter and latent variable inference. Preliminary experimental results on multivariate regression show the feasibility of the proposed model.	We prove the global existence, uniqueness, and positivity of solutions to the Cauchy problem, with general initial data, for a class of generalized Boltzmann models with dissipative collisions.	eng_Latn	34,049
A Bayesian Approach to Explaining Sequential Adoption of Components of a Technological Package	Agricultural innovations are often promoted as a package—a new seed variety, a recommended fertilizer application, and other recommended cultivation practices. Nevertheless, many farmers adopt pieces of the package rather than the whole, in a sequential fashion. This paper presents a behavioral model which explains sequential adoption as a consequence of learning by adopting farmers. In order to learn more about the entire technological package, the farmer may adopt a part of the package. The model is shown to be consistent with observed patterns of sequential adoption.	Many data analysis problems involve an investigation of relationships between attributes in heterogeneous databases, where different prediction models can be more appropriate for different regions. We propose a technique of integrating global and local random subspace ensemble. We performed a comparison with other well known combining methods on standard benchmark datasets and the proposed technique gave better accuracy.	eng_Latn	34,050
Exponential Stochastic Cellular Automata for Massively Parallel Inference	We propose an embarrassingly parallel, memory ecient inference algorithm for latent variable models in which the complete data likelihood is in the exponential family. The algorithm is a stochastic cellular automaton and converges to a valid maximum a posteriori fixed point. Applied to latent Dirichlet allocation we find that our algorithm is over an order or magnitude faster than the fastest current approaches. A simple C++/MPI implementation on a 20-node Amazon EC2 cluster samples at more than 1 billion tokens per second. We process 3 billion documents and achieve predictive power competitive with collapsed Gibbs sampling and variational inference.	Abstract The hand preferences of 58 monkeys (Macaca mulatta) on two tactile discrimination tasks are analysed in the light of the binomial model proposed by Annett [1].	eng_Latn	34,051
Exact Incremental and Decremental Learning for LS-SVM	In this paper, we present a novel incremental and decremental learning method for the least-squares support vector machine (LS-SVM). The goal is to adapt a pre-trained model to changes in the training dataset, without retraining the model on all the data, where the changes can include addition and deletion of data samples. We propose a provably exact method where the updated model is exactly the same as a model trained from scratch using the entire (updated) training dataset. Our proposed method only requires access to the updated data samples, the previous model parameters, and a unique, fixed-size matrix that quantifies the effect of the previous training dataset. Our approach can significantly reduce the storage requirement of model updating, preserve the privacy of unchanged training samples without loss of model accuracy, and enhance the computational efficiency. Experiments on real-world image dataset validate the effectiveness of our proposed method.	We present a scalable approach to perform- ::: ing approximate fully Bayesian inference in ::: generic state space models. The proposed ::: method is an alternative to particle MCMC ::: that provides fully Bayesian inference of both ::: the dynamic latent states and the static pa- ::: rameters of the model. We build up on re- ::: cent advances in computational statistics that ::: combine variational methods with sequential ::: Monte Carlo sampling and we demonstrate ::: the advantages of performing full Bayesian in- ::: ference over the static parameters rather than ::: just performing variational EM approxima- ::: tions. We illustrate how our approach enables ::: scalable inference in multivariate stochastic ::: volatility models and self-exciting point pro- ::: cess models that allow for ﬂexible dynamics ::: in the latent intensity function.	eng_Latn	34,052
Inferring web preferences from mobile	In one embodiment, a server providing an on-line service identifies a change associated with a mobile computing device of a user of the on-line service, the on-line service being accessible to the user through a website hosted by the system; the server also in response to the change and without manual user input from the user, modifies aspects of web pages of the website that are associated with use of the on-line service by the user.	We present a scalable approach to perform- ::: ing approximate fully Bayesian inference in ::: generic state space models. The proposed ::: method is an alternative to particle MCMC ::: that provides fully Bayesian inference of both ::: the dynamic latent states and the static pa- ::: rameters of the model. We build up on re- ::: cent advances in computational statistics that ::: combine variational methods with sequential ::: Monte Carlo sampling and we demonstrate ::: the advantages of performing full Bayesian in- ::: ference over the static parameters rather than ::: just performing variational EM approxima- ::: tions. We illustrate how our approach enables ::: scalable inference in multivariate stochastic ::: volatility models and self-exciting point pro- ::: cess models that allow for ﬂexible dynamics ::: in the latent intensity function.	eng_Latn	34,053
Modularization in Bayesian Analysis, with Emphasis on Analysis of Computer Models ∗	Bayesian analysis incorporates different sources of information into a single analysis through Bayes theorem. When one or more of the sources of information are suspect (e.g., if the model assumed for the information is viewed as quite possibly being significantly flawed), there can be a concern that Bayes theorem allows this suspect information to overly influence the other sources of information. We consider a variety of situations in which this arises, and give methodological suggestions for dealing with the problem. After consideration of some pedagogical examples of the phenomenon, we focus on the interface of statistics and the development of complex computer models of processes. Three testbed computer models are considered, in which this type of issue arises.	CITATION: Bassi, A. 2016. Moving towards integrated policy formulation and evaluation : the green economy model. Environmental and Climate Technologies, 16(1):5-19, doi:10.1515/rtuect-2015-0009.	eng_Latn	34,054
Sulla correttezza probabilistica dei sistemi esperti	In this paper a theoretical environment, enhancing the work of P. Hajek, is introduce to evaluate the statistical soundness of deductions in rule based expert systems. Assuming the system to be flat the problem of constructing a suitable knowledge base, in order to have probabilistically sound results, has been solved by using the Mobius function; it is also shown a construction of a log linear representation with the same technique.	If a known linear system is excited by Gaussian white noise, the calculation of the output covariance of the system is relatively straightforward. This paper considers the harder converse problem, that of passing from a known covariance to a system which will generate it. The problem is solved for covariancesR y (t, τ) with \|R y (t, t)\| < ∞ for allt and such that they-process is Gauss-Markov, i.e., it may be obtained as the output of a linear finite-dimensional system excited by white noise.	ita_Latn	34,055
Combination of Wigner-Ville distribution and its application to blind parameter estimation of frequency-hopping signals	To estimate the parameters of frequency-hopping signals,a new combination of Wigner-Ville distribution based on frequency decomposition is put forward according to the characteristics of frequency-hopping signals.First,a multi-component frequency-hopping signal is decomposed into multiple single-component signals with a group of band filters.Then the WVD of each component is summed up to form a new time-frequency distribution.Theoretical analysis and simulation result show that the new combination of Wigner-Ville distribution is effective to the reduction in cross-term interference of frequency-hopping signals.Compared with the existing method,this new method preserves a higher time-frequency resolution.Therefore,it is more suitable to time-frequency analysis and parameter estimation of frequency-hopping signals.	We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the procedure is point-wise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecorded variables are not caused by recorded variables; and (c) dependencies are linear. We compare the procedure with standard approaches over a variety of simulated structures and sample sizes, and illustrate its practical value with brief studies of social science data sets. Finally, we consider generalizations for non-linear systems.	eng_Latn	34,056
Eigenfunctions heuristics for self-conjugate priors	Operator Theoretic Perspectives on Bayesian Inference	Group element not taken to its inverse by any automorphism	eng_Latn	34,057
etude de mod \ ` eles \ ` a base de r \ ' eseaux bay \ ' esiens pour l ' aide au diagnostic de tumeurs c \ ' er \ ' ebrales .	Bayesian Network Learning with Parameter Constraints	Effects of actinobacteria on plant disease suppression and growth promotion	fra_Latn	34,058
Causal Discovery with Continuous Additive Noise Models	Learning Bayesian Networks is NP-Complete	Reasoning about Human Participation in Self-Adaptive Systems	eng_Latn	34,059
boosted statistical relational learners .	Discriminative Probabilistic Models for Relational Data	Probabilistic Horn abduction and Bayesian networks	eng_Latn	34,060
The most probable database problem	Probabilistic databases	The crooked nose	eng_Latn	34,061
The monte carlo database system: Stochastic analysis close to the data	MCDB: a monte carlo approach to managing uncertain data	MPE and Partial Inversion in Lifted Probabilistic Variable Elimination	eng_Latn	34,062
Compiling Bayesian Networks Using Variable Elimination	bucket elimination : a unifying framework for probabilistic inference .	Directional Resolution: The Davis-Putnam Procedure, Revisited	eng_Latn	34,063
A Bayesian Approach to Causal Discovery	Learning Bayesian Networks with Discrete Variables from Data	intelligent evolutionary design : a new approach to optimizing complex engineering systems and its application to designing heat exchangers .	eng_Latn	34,064
fast ε - free inference of simulation models with bayesian conditional density estimation .	Markov chain Monte Carlo without likelihoods	The power of a nod and a glance: Envelope vs. emotional feedback in animated conversational agents	eng_Latn	34,065
Parameter Space Noise for Exploration	auto - encoding variational bayes .	validity and reliability tests in case study research : a literature review with ` ` hands - on ' ' applications for each research phase .	eng_Latn	34,066
bayesian computation via markov chain monte .	Markov chains for exploring posterior distributions	maximum likelihood from incomplete data via the em - algorithm plus discussions on the paper .	eng_Latn	34,067
Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations	a family of algorithms for approximate bayesian inference .	Accelerated, Parallel and Proximal Coordinate Descent	eng_Latn	34,068
Compositional Vector Space Models for Knowledge Base Completion	Random Walk Inference and Learning in A Large Scale Knowledge Base	Radiation protection recommendations for I-131 thyrotoxicosis, thyroid cancer and phaeochromocytoma patients	eng_Latn	34,069
Learning graphical models for stationary time series	The capacity of low-density parity-check codes under message passing decoding	Probabilistic reasoning in intelligent systems: Networks of plausible inference	eng_Latn	34,070
Cognitive computation: A Bayesian machine case study	Church: a language for generative models	Representation of Events in Nerve Nets and Finite Automata	eng_Latn	34,071
Bayesian structure learning in graphical models	A Well-Conditioned Estimator For Large Dimensional Covariance Matrices	Schemas for Narrative Generation Mined from Existing Descriptions of Plot	eng_Latn	34,072
Unsupervised prediction of citation influences	Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks	endocrowns : a retrospective patient series study , in an 8 - to - 19 - year period .	eng_Latn	34,073
Inferring User Preferences by Probabilistic Logical Reasoning over Social Networks	A Machine-Oriented Logic Based on the Resolution Principle	Learning Disabilities	eng_Latn	34,074
A differential approach to inference in Bayesian networks	Graph-Based Algorithms for Boolean Function Manipulation	Transposable element polymorphisms recapitulate human evolution	eng_Latn	34,075
Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques	How deep is knowledge tracing?	Training data selection for acoustic modeling via submodular optimization of joint kullback-leibler divergence.	eng_Latn	34,076
The principles and practice of probabilistic programming	Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation	Neglected Infections of Poverty in the United States of America	eng_Latn	34,077
Bayesian Logic Programming: Theory and Tool	Inductive Logic Programming: Theory and Methods	FAST DUAL MINIMIZATION OF THE VECTORIAL TOTAL VARIATION NORM AND APPLICATIONS TO COLOR IMAGE PROCESSING	kor_Hang	34,078
Bayesian Optimization Algorithm	Learning Bayesian networks: The combination of knowledge and statistical data	An automated method of penetration testing	kor_Hang	34,079
Time-varying dynamic bayesian networks	Modeling changing dependency structure in multivariate time series	Using graph cut segmentation for food calorie measurement	eng_Latn	34,080
Bayesian Biosurveillance of Disease Outbreaks	Object-Oriented Bayesian Networks	Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States	kor_Hang	34,081
System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics	A tutorial on hidden Markov models and selected applications in speech recognition	Markov Chain Sampling Methods for Dirichlet Process Mixture Models	eng_Latn	34,082
SPOOK: A System for Probabilistic Object-Oriented Knowledge Representation	Context-Specific Independence in Bayesian Networks	Graph-Based Algorithms for Boolean Function Manipulation	eng_Latn	34,083
Approximate Bayesian Computation (ABC) in practice	Population growth of human Y chromosomes : a study of Y chromosome microsatellites	Options Discovery with Budgeted Reinforcement Learning	eng_Latn	34,084
Bayesian active model selection with an application to automated audiometry	Structure Discovery in Nonparametric Regression through Compositional Kernel Search	Types of study in medical research: part 3 of a series on evaluation of scientific publications.	eng_Latn	34,085
Bayesian model choice based on Monte Carlo estimates of posterior model probabilities	approximate bayesian inference with the weighted likelihood bootstrap .	Classification of Lung Tumors by using Deep Learning	eng_Latn	34,086
Efficient Bayesian Network Learning for System Optimization in Reliability Engineering	Learning Bayesian networks: The combination of knowledge and statistical data	Chapter 2 Rigid Body Registration	eng_Latn	34,087
Max-value Entropy Search for Efficient Bayesian Optimization	Random features for large-scale kernel machines	Next-generation digital forensics	kor_Hang	34,088
bayesian nonparametric inference of switching dynamic linear models .	A tutorial on hidden Markov models and selected applications in speech recognition	Methionine oxidation, alpha-synuclein and Parkinson's disease.	eng_Latn	34,089
deep ensemble bayesian active learning : ad - .	A mathematical theory of communication: Meaning, information, and topology	Variational Bayesian Inference with Stochastic Search	eng_Latn	34,090
Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks	Learning Optimal Bayesian Networks: A Shortest Path Perspective	Cross-Entropy for Monte-Carlo Tree Search	kor_Hang	34,091
Causal Structure Learning and Inference: A Selective Review	A modification of the PC algorithm yielding order-independent skeletons	The Bayesian Structural EM Algorithm	kor_Hang	34,092
modeling individualization in a bayesian networks implementation of knowledge tracing .	Knowledge tracing: Modeling the acquisition of procedural knowledge	The meaning and use of the area under a receiver operating characteristic (ROC) curve.	eng_Latn	34,093
Entropie causality anc greedy minimum entropy coupling	Nonlinear causal discovery with additive noise models	D³ Data-Driven Documents	kor_Hang	34,094
Bayesian model choice based on Monte Carlo estimates of posterior model probabilities	approximate bayesian inference with the weighted likelihood bootstrap .	delayed rejection in reversible .	eng_Latn	34,095
Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling	Finding Structure in Time	Online EM for Unsupervised Models	eng_Latn	34,096
Empirical evaluation of scoring functions for Bayesian network model selection	Tabu Search: A Tutorial	Personal Responsibility And Obesity: A Constructive Approach To A Controversial Issue	eng_Latn	34,097
On the Complexity and Approximation of Binary Evidence in Lifted Inference	Conditioning in first-order knowledge compilation and lifted probabilistic inference	Towards Health (Aware) Recommender Systems	eng_Latn	34,098
A Methodology for Quality-Based Selection of Internet Data Sources in Maritime Domain	Explanation Methods for Bayesian Networks : review and application to a maritime scenario	Medical Image Registration	yue_Hant	34,099

Bayesian analysis with consideration of data uncertainty in a specific scenario

Abstract In this paper data uncertainty in a specific scenario is modeled with Berkson and Classical errors. With consideration of uncertainty three methods within Bayesian framework are presented to update the failure probability with Beta-Binomial model. It shows that the three methods have their posteriors in the same form of weighted Beta distributions, but the weights are different for each method. Approximation to the mixed posteriors has been proposed and demonstrated by computation results. Moreover, comparison and illustration of the three methods are made based on case study and analytical analysis, which suggest that the LO method with Classical error model be more appropriate in similar applications.

According to the principle and algorithm of BP neural network,the structure of vibrating sieve can be determined.The BP neural network model for fault diagnosing is obtained by taking the characteristic variables of operating state of vibrating sieve as the input of neural network and using the Matlab neural network toolbox for the network training.The experimental result shows that the method can accurately diagnose the fault of vibrating sieve.

eng_Latn

34,000

Ergonomics Solutions in Dental Practice

Increased modernization and immense use of technology in dental practice has exposed dentists to high risk of poor postural habits thus leading to high risk of musculoskeletal disorders like low back pain, tennis elbow and carpel tunnel syndrome etc. This review paper highlights the importance of ergonomic solutions in dental practice.

Abstract In this paper, identification of the joint probability dentisity function (PDF) from missing data is considered. The model of PDF is Gaussian mixture. It is well known that the expectation-maximization (EM) algorithm is useful for the identification of Gaussian mixture. Here it is extended to the case of missing elements of the observations. It will be shown that, after identifying the PDF model, it is easy to estimate the missing elements as well as the system output variable.

eng_Latn

34,001

Estimation of Reliability Parameters Under Incomplete Primary Information

We consider the procedure for small-sample estimation of reliability parameters. The main shortcomings of the classical methods and the Bayesian approach are analyzed. Models that find robust Bayesian estimates are proposed. The sensitivity of the Bayesian estimates to the choice of the prior distribution functions is investigated using models that find upper and lower bounds. The proposed models reduce to optimization problems in the space of distribution functions.

The present study develops a non-parametric Bayesian network (NPBN) model to predict the corrosion depth on buried pipelines using the pipeline age and local soil properties. The dependence structu...

eng_Latn

34,002

Iterative Data Completion for Limited Angle Tomography using Filtered Backprojection

When the range of projection angles is limited, tomographic reconstruction suffers from artifacts caused by incomplete data. One can consider a data completion technique, which estimates projection data at unobserved angles using a prior knowledge or mathematical exploration, but the result is often not improved; the improvement by the data completion often undermined by the artifacts by inaccurate estimation, In this paper, we propose an iterative method, which computes projection data at unobserved angles by using the current estimate on the image, links the computed projection data to the observed ones by using the consistence condition of Radon transform, and reconstruct the next estimate on the image by filtered backprojection. The proposed method does not require a prior knowledge on the image, and has much faster approximation rate than the expectation maximization method. The performance of the proposed method was tested through several simulation studies.

We present a scalable approach to perform- ::: ing approximate fully Bayesian inference in ::: generic state space models. The proposed ::: method is an alternative to particle MCMC ::: that provides fully Bayesian inference of both ::: the dynamic latent states and the static pa- ::: rameters of the model. We build up on re- ::: cent advances in computational statistics that ::: combine variational methods with sequential ::: Monte Carlo sampling and we demonstrate ::: the advantages of performing full Bayesian in- ::: ference over the static parameters rather than ::: just performing variational EM approxima- ::: tions. We illustrate how our approach enables ::: scalable inference in multivariate stochastic ::: volatility models and self-exciting point pro- ::: cess models that allow for ﬂexible dynamics ::: in the latent intensity function.

eng_Latn

34,003

Paratesticular embyronal rhabdomyosarcoma in an adolescent: A rare case report

Embyronal rhabdomyosarcoma (RMS) accounts for approximately 49% of all RMS. After head and neck, this tumor is most commonly found in genitourinary region, which includes paratesticular RMS. Paratesticular RMS is rare constituting 4-7% of all RMS in children and young adults. It has been regarded as highly malignant tumor with frequent recurrence. The management protocol is of multimodal approach of surgery, chemo, and radiotherapy. We herein report a case of left paratesticular RMS in an 18-year-old male, which posed a diagnostic dilemma clinically and by imaging. Histopathology with added immunohistochemistry brought out the confirmatory diagnosis. The patient was successfully treated and on follow-up is disease free until date.

ABSTRACTLongitudinal data are commonly modeled with the normal mixed-effects models. Most modeling methods are based on traditional mean regression, which results in non robust estimation when suffering extreme values or outliers. Median regression is also not a best choice to estimation especially for non normal errors. Compared to conventional modeling methods, composite quantile regression can provide robust estimation results even for non normal errors. In this paper, based on a so-called pseudo composite asymmetric Laplace distribution (PCALD), we develop a Bayesian treatment to composite quantile regression for mixed-effects models. Furthermore, with the location-scale mixture representation of the PCALD, we establish a Bayesian hierarchical model and achieve the posterior inference of all unknown parameters and latent variables using Markov Chain Monte Carlo (MCMC) method. Finally, this newly developed procedure is illustrated by some Monte Carlo simulations and a case analysis of HIV/AIDS clinical...

eng_Latn

34,004

the time has come bayesian methods for data analysis in the organizational sciences .

What to believe : Bayesian methods for data analysis

Corticofugal modulation of the midbrain frequency map in the bat auditory system

eng_Latn

34,005

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Practical Bayesian Optimization of Machine Learning Algorithms

Power electronics and future marine electrical systems

eng_Latn

34,006

Bayesian Just-So Stories in Psychology and Neuroscience

how persuasive is a good fit ? a comment on theory testing .

Single Image Layer Separation Using Relative Smoothness

eng_Latn

34,007

Survey on data science with population-based algorithms

sequential monte carlo samplers .

an adaptive level set approach for incompressible two - phase flows 1 .

eng_Latn

34,008

Group-Based Trajectory Modeling in Clinical Research

Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study

Design and development of IGBT resonant inverters for domestic induction heating applications

eng_Latn

34,009

Object-oriented Bayesian networks for detection of lane change maneuvers

Object-Oriented Bayesian Networks

different emotional reactions to different groups : a sociofunctional threat - based approach to " prejudice " .

eng_Latn

34,010

Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search

Derivative-free optimization: a review of algorithms and comparison of software implementations

Measuring isotropic subsurface light transport

eng_Latn

34,011

Describing Visual Scenes using Transformed Dirichlet Processes

A Bayesian hierarchical model for learning natural scene categories

Effects of Brief and Sham Mindfulness Meditation on Mood and Cardiovascular Variables

eng_Latn

34,012

Selective Inference for Group-Sparse Linear Models

Model selection and estimation in regression with grouped variables

Difficulties in Simulating the Internet

eng_Latn

34,013

Spiking Neural Networks: Principles and Challenges

Bayesian inference with probabilistic population codes

A gradient descent rule for spiking neurons emitting multiple spikes

eng_Latn

34,014

Optimal Proposal Distributions and Adaptive MCMC

sequential monte carlo samplers .

Prp40 and early events in splice site definition.

eng_Latn

34,015

Coverage directed test generation for functional verification using Bayesian networks

Learning Dynamic Bayesian Networks

An Enhanced Grid Current Compensator for Grid-Connected Distributed Generation Under Nonlinear Loads and Grid Voltage Distortions

eng_Latn

34,016

An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions

The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

Genomic selection for wheat traits and trait stability

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34,017

Asymptotically Exact, Embarrassingly Parallel MCMC

Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring

Protective activity of ketoprofen lysine salt against the pulmonary effects induced by bradykinin in guinea-pigs

eng_Latn

34,018

Decision support system for the glaucoma using Gabor transformation

Learning Bayesian networks: The combination of knowledge and statistical data

Augmentation of the Columella-Labial Angle to Prevent the ``Smiling Deformity'' in Rhinoplasty

eng_Latn

34,019

On Tikhonov Regularization, Bias and Variance in Nonlinear System Identification

bayesian methods for adaptive models thesis by second edition bayesian methods for adaptive models .

Based on the Texture Analysis to Inspect the Tread Worn Status on the Tire

eng_Latn

34,020

Copula bayesian networks

An Introduction to Variational Methods for Graphical Models

Intelligence Measure of Cognitive Radios with Learning Capabilities

eng_Latn

34,021

Learning Symbolic Models of Stochastic Domains

STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving

The use of CORDIC in software defined radios: a tutorial

eng_Latn

34,022

Nested Sequential Monte Carlo Methods

Monte Carlo smoothing for non-linear time series

On sequential Monte Carlo sampling methods for Bayesian filtering

eng_Latn

34,023

Model selection in ecology and evolution

Bayesian Model Selection and Model Averaging

Design and Analysis of Swapped Port Coupler and Its Application in a Miniaturized Butler Matrix

eng_Latn

34,024

Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

Design of water cooled electric motors using CFD and thermography techniques

eng_Latn

34,025

Sparse nonparametric density estimation in high dimensions using the rodeo

Bayesian Density Estimation and Inference Using Mixtures

Modeling and control design of Magnetic levitation system

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34,026

On a New Improvement-Based Acquisition Function for Bayesian Optimization

Adaptive simulated annealing (ASA): Lessons learned

Linear and quadratic time-frequency signal representations

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34,027

A Bayesian network framework for reject inference

The Foundations of Cost-Sensitive Learning

Trauma-and Attachment-Informed Sensory Integration Assessment and Intervention n

eng_Latn

34,028

On the half-Cauchy prior for a global scale parameter

Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction

The enactment of socio-technical transition pathways: A reformulated typology and a comparative multi-level analysis of the German and UK low-carbon electricity transitions (1990–2014)

eng_Latn

34,029

Monte Carlo methods for massively parallel computers

Monte Carlo Methods in Classical Statistical Physics

LTE-based passive device-free crowd density estimation

eng_Latn

34,030

Probabilistic inference as a model of planned behavior

Anytime Point-Based Approximations for Large POMDPs

Health Working Papers No . 63 Value in Pharmaceutical Pricing

eng_Latn

34,031

Bayesian Model Averaging: A Tutorial

Bayesian Variable Selection

Model of Differentiation between Normal and Abnormal Heart Sounds in Using the Discrete Wavelet Transform

kor_Hang

34,032

Bayesian Ascent: A Data-Driven Optimization Scheme for Real-Time Control With Application to Wind Farm Power Maximization

Derivative-free optimization: a review of algorithms and comparison of software implementations

A navigation model for exploring scientific workflow provenance graphs

kor_Hang

34,033

A Novel Experimental Platform for In-Vessel Multi-Chemical Molecular Communications

Molecular MIMO: From Theory to Prototype

Subjective bayesian methods for rule-based inference systems

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34,034

Assessing the Value of the Threshold Parameter in the Weibull Distribution Using Bayes Paradigm

The Weibull distribution represents a wide variety of situations. Usually, the distribution is considered as a two-parameter family with a scale, and a shape parameter. If, however, the given data reflect additional information in the form of a minimum guarantee, a positive value away from zero, it is better to go for a three-parameter model with the additional parameter known as the threshold. The threshold parameter is often very important, but increases the complexity of the model. Arbitrarily going for the three-parameter form is not advisable unless it is really required by the data. This article attempts to make a simulation-based Bayesian study for checking if the threshold parameter can be taken to be zero or positive in situations representing the two models. We study the compatibility of the models for the given data set. We conduct the posterior simulation in each case using Gibbs sampling.

The main objective of this article is to study dynamic of the ::: three-dimensional Boussinesq equations with the periodic boundary ::: condition.We prove that when the Rayleigh number $R$ crosses the ::: first critical Rayleigh number $R_c$, the Rayleigh-Benard problem ::: bifurcates from the basic state to an global attractor $\Sigma$, which is homeomorphic to $S^3$.

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34,035

Height Reconstruction inHighly Sloped AreaUsingMulti-frequency InSARData

Withtheemerging ofmulti-frequency InSARsystem, extraction of3-Dandvariation information ofearth surface usingmulti-frequency InSARdatahasdrawnmore attention. Methodsbasedon Maximum-Likelihood Estimation (MLE),combining withsomeconventional phaseunwrapping algorithm canbeusedtoachieve this purpose. Inthispaper, MLE integrated withweighted multigrid phase-unwrapping method forhigh-sloped terrain profile reconstruction ispresented andstudied indetail. The performance ofthis methodisalsoanalyzed. Experiment result using simulated InSARdatashowsthat themethod canbe usedtoreconstruct high-sloped terrain with relatively high accuracy. IndexTerms-InSAR,height reconstruction, MLE, phase-unwrapping

AbstractInterval-censored survival data arise often in medical applications and clinical trials [Wang L, Sun J, Tong X. Regression analyis of case II interval-censored failure time data with the additive hazards model. Statistica Sinica. 2010;20:1709–1723]. However, most of existing interval-censored survival analysis techniques suffer from challenges such as heavy computational cost or non-proportionality of hazard rates due to complicated data structure [Wang L, Lin X. A Bayesian approach for analyzing case 2 interval-censored data under the semiparametric proportional odds model. Statistics & Probability Letters. 2011;81:876–883; Banerjee T, Chen M-H, Dey DK, et al. Bayesian analysis of generalized odds-rate hazards models for survival data. Lifetime Data Analysis. 2007;13:241–260]. To address these challenges, in this paper, we introduce a flexible Bayesian non-parametric procedure for the estimation of the odds under interval censoring, case II. We use Bernstein polynomials to introduce a prior for m...

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34,036

Analysis of Wine Bouquet Components Using Headspace Solid‐Phase Microextraction‐Capillary Gas Chromatography

Headspace solid-phase microextraction (HS/SPME) was studied and optimized for the capillary gas chromatographic (CGC) analysis of wine aroma compounds. The results were compared with those obtained using the direct sampling mode (DI/SPME) and using liquid/liquid extraction with Kaltron. The aromatic patterns obtained by HS/SPME-CGC were applied to the chemometric classification of wine varieties. The HS/SPME-CGC standard additional method is an appropriate technique for the quantitative analysis of volatile wine aroma compounds.

Two-choice response times are a common type of data, and much research has been devoted to the development of process models for such data. However, the practical application of these models is notoriously complicated, and flexible methods are largely nonexistent. We combine a popular model for choice response times—the Wiener diffusion process—with techniques from psychometrics in order to construct a hierarchical diffusion model. Chief among these techniques is the application of random effects, with which we allow for unexplained variability among participants, items, or other experimental units. These techniques lead to a modeling framework that is highly flexible and easy to work with. Among the many novel models this statistical framework provides are a multilevel diffusion model, regression diffusion models, and a large family of explanatory diffusion models. We provide examples and the necessary computer code.

kor_Hang

34,037

Broadening of the absorption spectrum in a p-type InGaAs - InAlAs coupled quantum well

The intervalence subband absorption of normally incident infrared radiation in a p-type InGaAs - InAlAs coupled quantum well (CQW) is theoretically investigated by the multiband effective mass formalism. We calculate valence subband structures, intervalence subband transition matrix elements and the absorption coefficient spectrum in the CQW which consists of a wider well, a thinner well and a barrier between. We demonstrate that the absorption coefficient profile can be tailored to suit one's need by careful control of the flexible design parameters given to the valence band CQW structure. For the application to an infrared detector with a flat absorptivity over a wider wavelength range than the conventional detectors, the CQW is designed to have broadened absorption spectrum.

ABSTRACTLongitudinal data are commonly modeled with the normal mixed-effects models. Most modeling methods are based on traditional mean regression, which results in non robust estimation when suffering extreme values or outliers. Median regression is also not a best choice to estimation especially for non normal errors. Compared to conventional modeling methods, composite quantile regression can provide robust estimation results even for non normal errors. In this paper, based on a so-called pseudo composite asymmetric Laplace distribution (PCALD), we develop a Bayesian treatment to composite quantile regression for mixed-effects models. Furthermore, with the location-scale mixture representation of the PCALD, we establish a Bayesian hierarchical model and achieve the posterior inference of all unknown parameters and latent variables using Markov Chain Monte Carlo (MCMC) method. Finally, this newly developed procedure is illustrated by some Monte Carlo simulations and a case analysis of HIV/AIDS clinical...

eng_Latn

34,038

Bayesian Hierarchical Model for Identifying Changes in Gene Expression from Microarray Experiments

Recent developments in microarrays technology enable researchers to study simultaneously the expression of thousands of genes from one cell line or tissue sample. This new technology is often used to assess changes in mRNA expression upon a specified transfection for a cell line in order to identify target genes. For such experiments, the range of differential expression is moderate, and teasing out the modified genes is challenging and calls for detailed modeling. The aim of this paper is to propose a methodological framework for studies that investigate differential gene expression through microarrays technology that is based on a fully Bayesian mixture approach (Richardson and Green, 1997). A case study that investigated those genes that were differentially expressed in two cell lines (normal and modified by a gene transfection) is provided to illustrate the performance and usefulness of this approach.

It is the purpose of this paper to introduce a novel estimator for the extremal index of an instantaneous function {f(Xn)}n of a regenerative Harris Markov chain X, based on the renewal properties of the latter. The estimate proposed may be viewed as a "regenerative version" of the runs estimator, insofar as it measures the clustering tendency of high threshold exceedances within regeneration cycles. Strong consistency of this estimator is established under mild stochastic stability assumptions and a simulation result is displayed in the case when the underlying chain is the waiting process related to a simple M/M/1 queue.

eng_Latn

34,039

Statistical analysis of mixtures underlying probability of ruin

If the hypothesis on exponentially distributed claims in a risk (or surplus) model is untenable then, in many cases, the assumption that they are mixtures of two (or more) exponentials is a suitable substitute. In the first part of the paper tests of homogeneity for exponentially distributed claims are discussed and their properties are stated. The statistical properties of parameter estimations for such claims are also mentioned. In the second part the classical Cramer-Lundberg ruin model is discussed when claims are distributed as mixtures of exponentials. Our attention is focussed primarily on assesment of accuracy of approximations obtained. Then our results are compared to those already known.

Study classical general mathematics model bag fetch ball,arrange in an order,put ball not to enter case analytical method of problem,utilize the these problems analytical method to solve some classical and general probability and calculate the problem.

eng_Latn

34,040

Global Uncertainty and Sensitivity Analysis and Neighbourhoods

We shall briefly review recent progress in Global Quantitative Uncertainty and Sensitivity Analysis (UA/SA) techniques, relating these to multidimensional global calibration approaches of the “Monte Carlo filtering” type. Global quantitative techniques for Sensitivity Analysis (SA), that are based on the decomposition of the variance of the target model output, have received a considerable boost in recent years, due both to more efficient computational strategies and to a widening of their range of applications. Monte Carlo Filtering, and the GLUE (Generalised Likelihood Uncertainty Estimate) approach that derives from it are also promising tools to use in the presence of structural model uncertainty, as in the case of petroleum engineering that was the focus of the ECMI2002 mini-symposium “Advanced Mathematical Tools for Petroleum System Modelling”.

Living in a dwelling that is close to greenspace reduces youngsters’ risk for behaviors associated with neurobehavioral problems. This relationship varies with the type of behavior, the child’s age, and the proximity of the greenspace, according to a study conducted in an ongoing prospective birth cohort.

eng_Latn

34,041

Limited sampling model for the estimation of pharmacokinetic parameters in children.

Summary:A limited sampling model (LSM) is proposed for the first-time assessment of pharmacokinetic parameters (area under the concentration–time curve (AUC), Cmax, and T½) in children after a single oral dose of drug. Three drugs were evaluated in this study. The LSM was developed for each drug fro

This paper presents an extension of Petri net framework with imprecise temporal properties. We use possibility theory to represent imprecise time by time-stamping tokens and assigning durations to firing of the transitions. A method for approximation of an arbitrary temporal distribution with a set of possibilistic intervals is used to introduce the composition operation for two possibilistic temporal distributions. We developed a method to determining an effective enabling time of a transition with incoming tokens with possibilistic distributions. The utility of the proposed theory is illustrated using an example of an automated manufacturing system. The proposed approach is novel and has a broad utility beyond a timed Petri network and its applications.

eng_Latn

34,042

Modeling dichotomous item responses with free-knot splines

Item response theory (IRT) models are a class of generalized mixed effect (GME) models used by psychometricians to describe the response behavior of individuals to a set of categorically scored items. The typical assumptions of IRT are Unidimensionality(U) of the random effect; Conditional (or Local) Independence (CI), the item responses are independent given the random effect; and Monotonicity (M), the probability of a correct response is a non-decreasing function of the random effect. The simple parametric models available in the psychometric literature have proved to be too restrictive in many data sets. Non-parametric regression models are a powerful tool for the estimation of non-linear curves, and have been used in IRT as a flexible way to model the item response function. This paper develops a new method for the non-parametric estimation of item response functions based on reversible-jump Markov Chain Monte Carlo, and demonstrates the practicality of the method by examining two data sets.

We present a connectionist model designed for supervised learning of associated patterns, which is based on Kohonen’s self-organizing feature maps. While learning, the classification is performed on both inputs and desired associated ouputs. The learned weights are then used in exploitation phase to associate an input vector with an output one.

eng_Latn

34,043

Maximum likelihood estimation in hazard rate models with a change-point

The problem of estimation of parameters in hazard rate models with a change-point is considered. An interesting feature of this problem is that the likelihood function is unbounded. A maximum likelihood estimator of the change-point subject to a natural constraint is proposed, which is shown to be consistent.The limiting distributions are also derived.

This article advances a proposal that increases access to justice for valuable lawsuits that are currently discouraged by litigation costs. Our proposal converts claims with negative expected values into positive expected value claims by implementing a novel system involving flexible conditional multipliers. Our proposal has two components. First, under the proposed system a plaintiff is allowed to select a damage multiplier that determines the amount of damages the plaintiff receives if the litigation is successful. Second, courts select cases for litigation randomly with a probability inverse to the multiplier selected by the plaintiff.

eng_Latn

34,044

Femoral nerve compression syndrome with paresis of the quadriceps muscle caused by radiotherapy of malignant tumours. A report of four cases.

Four patients showed signs of femoral nerve compression with subsequent paresis of the quadriceps muscle, after radiation therapy of malignant tumours. The compression was caused by scar tissue due to radiation treatment of the inguinal region. The first symptom was radiating pain in the front of the thigh and lower leg which appeared 12-16 months after X-ray treatment. A decrease in the strength of quadriceps muscle occurred some months later. In one case the femoral nerve was decompressed, another patient was treated by an intradural phenolglycerin injection and one patient was treated with cortisone and oxiphenbutazone. In these cases the pain decreased considerably, but in one case only the paresis of the quadriceps muscle improved after treatment.

ABSTRACTLongitudinal data are commonly modeled with the normal mixed-effects models. Most modeling methods are based on traditional mean regression, which results in non robust estimation when suffering extreme values or outliers. Median regression is also not a best choice to estimation especially for non normal errors. Compared to conventional modeling methods, composite quantile regression can provide robust estimation results even for non normal errors. In this paper, based on a so-called pseudo composite asymmetric Laplace distribution (PCALD), we develop a Bayesian treatment to composite quantile regression for mixed-effects models. Furthermore, with the location-scale mixture representation of the PCALD, we establish a Bayesian hierarchical model and achieve the posterior inference of all unknown parameters and latent variables using Markov Chain Monte Carlo (MCMC) method. Finally, this newly developed procedure is illustrated by some Monte Carlo simulations and a case analysis of HIV/AIDS clinical...

eng_Latn

34,045

Efficient Simulation of Epistatic Interactions in Case-Parent Trios

Statistical approaches to evaluate interactions between single nucleotide polymorphisms (SNPs) and SNP-environment interactions are of great importance in genetic association studies, as susceptibility to complex disease might be related to the interaction of multiple SNPs and/or environmental factors. With these methods under active development, algorithms to simulate genomic data sets are needed to ensure proper type I error control of newly proposed methods and to compare power with existing methods. In this paper we propose an efficient method for a haplotype-based simulation of case-parent trios when the disease risk is thought to depend on possibly higher-order epistatic interactions or gene-environment interactions with binary exposures.

We present a scalable approach to perform- ::: ing approximate fully Bayesian inference in ::: generic state space models. The proposed ::: method is an alternative to particle MCMC ::: that provides fully Bayesian inference of both ::: the dynamic latent states and the static pa- ::: rameters of the model. We build up on re- ::: cent advances in computational statistics that ::: combine variational methods with sequential ::: Monte Carlo sampling and we demonstrate ::: the advantages of performing full Bayesian in- ::: ference over the static parameters rather than ::: just performing variational EM approxima- ::: tions. We illustrate how our approach enables ::: scalable inference in multivariate stochastic ::: volatility models and self-exciting point pro- ::: cess models that allow for ﬂexible dynamics ::: in the latent intensity function.

eng_Latn

34,046

ESPRESS—On efficient bistatic characterization of radar targets

In the modern radar target recognition, the model-based approach offers a flexible and computationally efficient way to characterize targets, since establishing an adequate target signature collection especially with bistatic measurements is impractical. Simulating such an extensive collection is arduous as well. This paper proposes a new method for the bistatic characterization of radar targets and radar response simulation: ESPRESS (Electromagnetic Signature Production from Renders Exploiting Scatterer Sets). We have implemented it entirely with commercial off-the-shelf (COTS) software. Our objective is to give the target a compact description, from which radar response—with arbitrary radar frequency and bandwidth, as well as transmitter and receiver positions—can be simulated efficiently. In this paper, we demonstrate that ESPRESS has the computational speed and adequate accuracy required in model-based radar target recognition.

We present a scalable approach to perform- ::: ing approximate fully Bayesian inference in ::: generic state space models. The proposed ::: method is an alternative to particle MCMC ::: that provides fully Bayesian inference of both ::: the dynamic latent states and the static pa- ::: rameters of the model. We build up on re- ::: cent advances in computational statistics that ::: combine variational methods with sequential ::: Monte Carlo sampling and we demonstrate ::: the advantages of performing full Bayesian in- ::: ference over the static parameters rather than ::: just performing variational EM approxima- ::: tions. We illustrate how our approach enables ::: scalable inference in multivariate stochastic ::: volatility models and self-exciting point pro- ::: cess models that allow for ﬂexible dynamics ::: in the latent intensity function.

eng_Latn

34,047

Probabilistic-Input, Noisy Conjunctive Models for Cognitive Diagnosis

Existing cognitive diagnosis models conceptualize attribute mastery status discretely as either mastery or nonmastery. This study proposes a different conceptualization of attribute mastery as a probabilistic concept, i.e., the probability of mastering a specific attribute for a person, and developing a probabilistic-input, noisy conjunctive (PINC) model, in which the probability of mastering an attribute for a person is a parameter to be estimated from data. And a higher-order version of the PINC model is used to consider the associations among attributes. The results of simulation studies revealed a good parameter recovery for the new models using the Bayesian method. The Examination for the Certificate of Proficiency in English (ECPE) data set was analyzed to illustrate the implications and applications of the proposed models. The results indicated that PINC models had better model-data fit, smaller item parameter estimates, and more refined estimates of attribute mastery.

Abstract Necessary conditions and iterative computational algorithms are obtained for the problem of choosing feedback gains to optimize a linear control system with a quadratic cost function. The results are obtained in abstract terms and cover a wide range of practical problems. A design example is given

eng_Latn

34,048

Infinite mixtures of multivariate Gaussian processes

This paper presents a new model called infinite mixtures of multivariate Gaussian processes, which can be used to learn vector-valued functions and applied to multitask learning. As an extension of the single multivariate Gaussian process, the mixture model has the advantages of modeling multimodal data and alleviating the computationally cubic complexity of the multivariate Gaussian process. A Dirichlet process prior is adopted to allow the (possibly infinite) number of mixture components to be automatically inferred from training data, and Markov chain Monte Carlo sampling techniques are used for parameter and latent variable inference. Preliminary experimental results on multivariate regression show the feasibility of the proposed model.

We prove the global existence, uniqueness, and positivity of solutions to the Cauchy problem, with general initial data, for a class of generalized Boltzmann models with dissipative collisions.

eng_Latn

34,049

A Bayesian Approach to Explaining Sequential Adoption of Components of a Technological Package

Agricultural innovations are often promoted as a package—a new seed variety, a recommended fertilizer application, and other recommended cultivation practices. Nevertheless, many farmers adopt pieces of the package rather than the whole, in a sequential fashion. This paper presents a behavioral model which explains sequential adoption as a consequence of learning by adopting farmers. In order to learn more about the entire technological package, the farmer may adopt a part of the package. The model is shown to be consistent with observed patterns of sequential adoption.

Many data analysis problems involve an investigation of relationships between attributes in heterogeneous databases, where different prediction models can be more appropriate for different regions. We propose a technique of integrating global and local random subspace ensemble. We performed a comparison with other well known combining methods on standard benchmark datasets and the proposed technique gave better accuracy.

eng_Latn

34,050

Exponential Stochastic Cellular Automata for Massively Parallel Inference

We propose an embarrassingly parallel, memory ecient inference algorithm for latent variable models in which the complete data likelihood is in the exponential family. The algorithm is a stochastic cellular automaton and converges to a valid maximum a posteriori fixed point. Applied to latent Dirichlet allocation we find that our algorithm is over an order or magnitude faster than the fastest current approaches. A simple C++/MPI implementation on a 20-node Amazon EC2 cluster samples at more than 1 billion tokens per second. We process 3 billion documents and achieve predictive power competitive with collapsed Gibbs sampling and variational inference.

Abstract The hand preferences of 58 monkeys (Macaca mulatta) on two tactile discrimination tasks are analysed in the light of the binomial model proposed by Annett [1].

eng_Latn

34,051

Exact Incremental and Decremental Learning for LS-SVM

In this paper, we present a novel incremental and decremental learning method for the least-squares support vector machine (LS-SVM). The goal is to adapt a pre-trained model to changes in the training dataset, without retraining the model on all the data, where the changes can include addition and deletion of data samples. We propose a provably exact method where the updated model is exactly the same as a model trained from scratch using the entire (updated) training dataset. Our proposed method only requires access to the updated data samples, the previous model parameters, and a unique, fixed-size matrix that quantifies the effect of the previous training dataset. Our approach can significantly reduce the storage requirement of model updating, preserve the privacy of unchanged training samples without loss of model accuracy, and enhance the computational efficiency. Experiments on real-world image dataset validate the effectiveness of our proposed method.

We present a scalable approach to perform- ::: ing approximate fully Bayesian inference in ::: generic state space models. The proposed ::: method is an alternative to particle MCMC ::: that provides fully Bayesian inference of both ::: the dynamic latent states and the static pa- ::: rameters of the model. We build up on re- ::: cent advances in computational statistics that ::: combine variational methods with sequential ::: Monte Carlo sampling and we demonstrate ::: the advantages of performing full Bayesian in- ::: ference over the static parameters rather than ::: just performing variational EM approxima- ::: tions. We illustrate how our approach enables ::: scalable inference in multivariate stochastic ::: volatility models and self-exciting point pro- ::: cess models that allow for ﬂexible dynamics ::: in the latent intensity function.

eng_Latn

34,052

Inferring web preferences from mobile

In one embodiment, a server providing an on-line service identifies a change associated with a mobile computing device of a user of the on-line service, the on-line service being accessible to the user through a website hosted by the system; the server also in response to the change and without manual user input from the user, modifies aspects of web pages of the website that are associated with use of the on-line service by the user.

We present a scalable approach to perform- ::: ing approximate fully Bayesian inference in ::: generic state space models. The proposed ::: method is an alternative to particle MCMC ::: that provides fully Bayesian inference of both ::: the dynamic latent states and the static pa- ::: rameters of the model. We build up on re- ::: cent advances in computational statistics that ::: combine variational methods with sequential ::: Monte Carlo sampling and we demonstrate ::: the advantages of performing full Bayesian in- ::: ference over the static parameters rather than ::: just performing variational EM approxima- ::: tions. We illustrate how our approach enables ::: scalable inference in multivariate stochastic ::: volatility models and self-exciting point pro- ::: cess models that allow for ﬂexible dynamics ::: in the latent intensity function.

eng_Latn

34,053

Modularization in Bayesian Analysis, with Emphasis on Analysis of Computer Models ∗

Bayesian analysis incorporates different sources of information into a single analysis through Bayes theorem. When one or more of the sources of information are suspect (e.g., if the model assumed for the information is viewed as quite possibly being significantly flawed), there can be a concern that Bayes theorem allows this suspect information to overly influence the other sources of information. We consider a variety of situations in which this arises, and give methodological suggestions for dealing with the problem. After consideration of some pedagogical examples of the phenomenon, we focus on the interface of statistics and the development of complex computer models of processes. Three testbed computer models are considered, in which this type of issue arises.

CITATION: Bassi, A. 2016. Moving towards integrated policy formulation and evaluation : the green economy model. Environmental and Climate Technologies, 16(1):5-19, doi:10.1515/rtuect-2015-0009.

eng_Latn

34,054

Sulla correttezza probabilistica dei sistemi esperti

In this paper a theoretical environment, enhancing the work of P. Hajek, is introduce to evaluate the statistical soundness of deductions in rule based expert systems. Assuming the system to be flat the problem of constructing a suitable knowledge base, in order to have probabilistically sound results, has been solved by using the Mobius function; it is also shown a construction of a log linear representation with the same technique.

If a known linear system is excited by Gaussian white noise, the calculation of the output covariance of the system is relatively straightforward. This paper considers the harder converse problem, that of passing from a known covariance to a system which will generate it. The problem is solved for covariancesR y (t, τ) with |R y (t, t)| < ∞ for allt and such that they-process is Gauss-Markov, i.e., it may be obtained as the output of a linear finite-dimensional system excited by white noise.

ita_Latn

34,055

Combination of Wigner-Ville distribution and its application to blind parameter estimation of frequency-hopping signals

To estimate the parameters of frequency-hopping signals,a new combination of Wigner-Ville distribution based on frequency decomposition is put forward according to the characteristics of frequency-hopping signals.First,a multi-component frequency-hopping signal is decomposed into multiple single-component signals with a group of band filters.Then the WVD of each component is summed up to form a new time-frequency distribution.Theoretical analysis and simulation result show that the new combination of Wigner-Ville distribution is effective to the reduction in cross-term interference of frequency-hopping signals.Compared with the existing method,this new method preserves a higher time-frequency resolution.Therefore,it is more suitable to time-frequency analysis and parameter estimation of frequency-hopping signals.

We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the procedure is point-wise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecorded variables are not caused by recorded variables; and (c) dependencies are linear. We compare the procedure with standard approaches over a variety of simulated structures and sample sizes, and illustrate its practical value with brief studies of social science data sets. Finally, we consider generalizations for non-linear systems.

eng_Latn

34,056

Eigenfunctions heuristics for self-conjugate priors

Operator Theoretic Perspectives on Bayesian Inference

Group element not taken to its inverse by any automorphism

eng_Latn

34,057

etude de mod \ ` eles \ ` a base de r \ ' eseaux bay \ ' esiens pour l ' aide au diagnostic de tumeurs c \ ' er \ ' ebrales .

Bayesian Network Learning with Parameter Constraints

Effects of actinobacteria on plant disease suppression and growth promotion

fra_Latn

34,058

Causal Discovery with Continuous Additive Noise Models

Learning Bayesian Networks is NP-Complete

Reasoning about Human Participation in Self-Adaptive Systems

eng_Latn

34,059

boosted statistical relational learners .

Discriminative Probabilistic Models for Relational Data

Probabilistic Horn abduction and Bayesian networks

eng_Latn

34,060

The most probable database problem

Probabilistic databases

The crooked nose

eng_Latn

34,061

The monte carlo database system: Stochastic analysis close to the data

MCDB: a monte carlo approach to managing uncertain data

MPE and Partial Inversion in Lifted Probabilistic Variable Elimination

eng_Latn

34,062

Compiling Bayesian Networks Using Variable Elimination

bucket elimination : a unifying framework for probabilistic inference .

Directional Resolution: The Davis-Putnam Procedure, Revisited

eng_Latn

34,063

A Bayesian Approach to Causal Discovery

Learning Bayesian Networks with Discrete Variables from Data

intelligent evolutionary design : a new approach to optimizing complex engineering systems and its application to designing heat exchangers .

eng_Latn

34,064

fast ε - free inference of simulation models with bayesian conditional density estimation .

Markov chain Monte Carlo without likelihoods

The power of a nod and a glance: Envelope vs. emotional feedback in animated conversational agents

eng_Latn

34,065

Parameter Space Noise for Exploration

auto - encoding variational bayes .

validity and reliability tests in case study research : a literature review with ` ` hands - on ' ' applications for each research phase .

eng_Latn

34,066

bayesian computation via markov chain monte .

Markov chains for exploring posterior distributions

maximum likelihood from incomplete data via the em - algorithm plus discussions on the paper .

eng_Latn

34,067

Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations

a family of algorithms for approximate bayesian inference .

Accelerated, Parallel and Proximal Coordinate Descent

eng_Latn

34,068

Compositional Vector Space Models for Knowledge Base Completion

Random Walk Inference and Learning in A Large Scale Knowledge Base

Radiation protection recommendations for I-131 thyrotoxicosis, thyroid cancer and phaeochromocytoma patients

eng_Latn

34,069

Learning graphical models for stationary time series

The capacity of low-density parity-check codes under message passing decoding

Probabilistic reasoning in intelligent systems: Networks of plausible inference

eng_Latn

34,070

Cognitive computation: A Bayesian machine case study

Church: a language for generative models

Representation of Events in Nerve Nets and Finite Automata

eng_Latn

34,071

Bayesian structure learning in graphical models

A Well-Conditioned Estimator For Large Dimensional Covariance Matrices

Schemas for Narrative Generation Mined from Existing Descriptions of Plot

eng_Latn

34,072

Unsupervised prediction of citation influences

Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks

endocrowns : a retrospective patient series study , in an 8 - to - 19 - year period .

eng_Latn

34,073

Inferring User Preferences by Probabilistic Logical Reasoning over Social Networks

A Machine-Oriented Logic Based on the Resolution Principle

Learning Disabilities

eng_Latn

34,074

A differential approach to inference in Bayesian networks

Graph-Based Algorithms for Boolean Function Manipulation

Transposable element polymorphisms recapitulate human evolution

eng_Latn

34,075

Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques

How deep is knowledge tracing?

Training data selection for acoustic modeling via submodular optimization of joint kullback-leibler divergence.

eng_Latn

34,076

The principles and practice of probabilistic programming

Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation

Neglected Infections of Poverty in the United States of America

eng_Latn

34,077

Bayesian Logic Programming: Theory and Tool

Inductive Logic Programming: Theory and Methods

FAST DUAL MINIMIZATION OF THE VECTORIAL TOTAL VARIATION NORM AND APPLICATIONS TO COLOR IMAGE PROCESSING

kor_Hang

34,078

Bayesian Optimization Algorithm

Learning Bayesian networks: The combination of knowledge and statistical data

An automated method of penetration testing

kor_Hang

34,079

Time-varying dynamic bayesian networks

Modeling changing dependency structure in multivariate time series

Using graph cut segmentation for food calorie measurement

eng_Latn

34,080

Bayesian Biosurveillance of Disease Outbreaks

Object-Oriented Bayesian Networks

Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States

kor_Hang

34,081

System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics

A tutorial on hidden Markov models and selected applications in speech recognition

Markov Chain Sampling Methods for Dirichlet Process Mixture Models

eng_Latn

34,082

SPOOK: A System for Probabilistic Object-Oriented Knowledge Representation

Context-Specific Independence in Bayesian Networks

Graph-Based Algorithms for Boolean Function Manipulation

eng_Latn

34,083

Approximate Bayesian Computation (ABC) in practice

Population growth of human Y chromosomes : a study of Y chromosome microsatellites

Options Discovery with Budgeted Reinforcement Learning

eng_Latn

34,084

Bayesian active model selection with an application to automated audiometry

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

Types of study in medical research: part 3 of a series on evaluation of scientific publications.

eng_Latn

34,085

Bayesian model choice based on Monte Carlo estimates of posterior model probabilities

approximate bayesian inference with the weighted likelihood bootstrap .

Classification of Lung Tumors by using Deep Learning

eng_Latn

34,086

Efficient Bayesian Network Learning for System Optimization in Reliability Engineering

Learning Bayesian networks: The combination of knowledge and statistical data

Chapter 2 Rigid Body Registration

eng_Latn

34,087

Max-value Entropy Search for Efficient Bayesian Optimization

Random features for large-scale kernel machines

Next-generation digital forensics

kor_Hang

34,088

bayesian nonparametric inference of switching dynamic linear models .

A tutorial on hidden Markov models and selected applications in speech recognition

Methionine oxidation, alpha-synuclein and Parkinson's disease.

eng_Latn

34,089

deep ensemble bayesian active learning : ad - .

A mathematical theory of communication: Meaning, information, and topology

Variational Bayesian Inference with Stochastic Search

eng_Latn

34,090

Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks

Learning Optimal Bayesian Networks: A Shortest Path Perspective

Cross-Entropy for Monte-Carlo Tree Search

kor_Hang

34,091

Causal Structure Learning and Inference: A Selective Review

A modification of the PC algorithm yielding order-independent skeletons

The Bayesian Structural EM Algorithm

kor_Hang

34,092

modeling individualization in a bayesian networks implementation of knowledge tracing .

Knowledge tracing: Modeling the acquisition of procedural knowledge

The meaning and use of the area under a receiver operating characteristic (ROC) curve.

eng_Latn

34,093

Entropie causality anc greedy minimum entropy coupling

Nonlinear causal discovery with additive noise models

D³ Data-Driven Documents

kor_Hang

34,094

Bayesian model choice based on Monte Carlo estimates of posterior model probabilities

approximate bayesian inference with the weighted likelihood bootstrap .

delayed rejection in reversible .

eng_Latn

34,095

Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling

Finding Structure in Time

Online EM for Unsupervised Models

eng_Latn

34,096

Empirical evaluation of scoring functions for Bayesian network model selection

Tabu Search: A Tutorial

Personal Responsibility And Obesity: A Constructive Approach To A Controversial Issue

eng_Latn

34,097

On the Complexity and Approximation of Binary Evidence in Lifted Inference

Conditioning in first-order knowledge compilation and lifted probabilistic inference

Towards Health (Aware) Recommender Systems

eng_Latn

34,098

A Methodology for Quality-Based Selection of Internet Data Sources in Maritime Domain

Explanation Methods for Bayesian Networks : review and application to a maritime scenario

Medical Image Registration

yue_Hant

34,099