搜索结果: 1-15 共查到“理论统计学 Bayesian”相关记录161条 . 查询时间(0.092 秒)
Bayesian semiparametric analysis for two-phase studies of gene-environment interaction
Biased sampling colorectal cancer Dirichlet prior exposure enriched sampling gene-environment independence jointeffects multivariate categorical distribution spike and slab prior
2013/6/14
The two-phase sampling design is a cost-efficient way of collecting expensive covariate information on a judiciously selected subsample. It is natural to apply such a strategy for collecting genetic d...
Bayesian Multi-Dipole Modeling of Single MEG Topographies by Adaptive Sequential Monte Carlo Samplers
Magnetoencephalography inverse problem Multi-object estimation Multi-dipole models Adaptive Sequential Monte Carlo samplers
2013/6/14
We describe a novel Bayesian approach to the estimation of neural currents from a single distribution of magnetic field, measured by magnetoencephalography. We model neural currents as an unknown numb...
Bayesian Functional Generalized Additive Models with Sparsely Observed Covariates
auction data functional data analysis functional regression linear mixed models measurement error MCMC penalized splines variational inference
2013/6/14
The functional generalized additive model (FGAM) was recently proposed in McLean et al. (2012) as a more flexible alternative to the common functional linear model (FLM) for regressing a scalar on fun...
Informative Bayesian inference for the skew-normal distribution
Bayesian inference Gibbs sampling Markov Chain Monte Carlo Multivariate skew-normal distribution Stochastic representation of the skew-normal Uni
2013/6/14
Motivated by the analysis of the distribution of university grades, which is usually asymmetric, we discuss two informative priors for the shape parameter of the skew-normal distribution, showing that...
Mean field variational Bayesian inference for support vector machine classification
Approximate Bayesian inference variable selection missing data mixed model Markov chain Monte Carlo
2013/6/14
A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented. This representation allows circumvention of ma...
Revisiting Bayesian Blind Deconvolution
Blind deconvolution blind image deblurring variational Bayes sparse priors sparse estimation
2013/6/14
Blind deconvolution involves the estimation of a sharp signal or image given only a blurry observation. Because this problem is fundamentally ill-posed, strong priors on both the sharp image and blur ...
Joint likelihood calculation for intervention and observational data from a Gaussian Bayesian network
Gaussian Bayesian networks causal effects intervention data Fisher information
2013/6/13
Methodological development for the inference of gene regulatory networks from transcriptomic data is an active and important research area. Several approaches have been proposed to infer relationships...
This paper proposes an online tree-based Bayesian approach for reinforcement learning. For inference, we employ a generalised context tree model. This defines a distribution on multivariate Gaussian p...
Bayesian Modeling and MCMC Computation in Linear Logistic Regression for Presence-only Data
Bayesian modeling case-control design data augmentation logistic regres-sion Markov Chain Monte Carlo population prevalence presence-only data simulation
2013/6/13
Presence-only data are referred to situations in which, given a censoring mechanism, a binary response can be observed only with respect to on outcome, usually called \textit{presence}. In this work w...
Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components
Bayesian model averaging continuous ranked probability score ensemble calibration truncated normal distribution
2013/6/13
Bayesian model averaging (BMA) is a statistical method for post-processing forecast ensembles of atmospheric variables, obtained from multiple runs of numerical weather prediction models, in order to ...
Bayesian Manifold Regression
Asymptotics Contraction rates Dimensional-ity reduction Gaussian process Manifold learning Nonparametric Bayes Subspace learning
2013/6/13
There is increasing interest in the problem of nonparametric regression with high-dimensional predictors. When the number of predictors $D$ is large, one encounters a daunting problem in attempting to...
Best arm identification via Bayesian gap-based exploration
Best arm identification Bayesian gap-based exploration
2013/4/28
Bayesian approaches to optimization under bandit feedback have recently become quite popular in the machine learning community. Methods of this type have been found to have not only very good empirica...
Two General Methods for Population Pharmacokinetic Modeling: Non-Parametric Adaptive Grid and Non-Parametric Bayesian
Population pharmacokinetic modeling non-parametric maximum likelihood Bayesian Stick-breaking Pmetrics RJags
2013/5/2
Population pharmacokinetic (PK) modeling methods can be statistically classified as either parametric or nonparametric (NP). Each classification can be divided into maximum likelihood (ML) or Bayesian...
A Robust Bayesian Dynamic Linear Model to Detect Abrupt Changes in an Economic Time Series: The Case of Puerto Rico
Dynamic Models Consumer Price Index Bayesian Robustness
2013/4/28
Economic indicators time series are usually complex with high frequency data. The traditional time series methodology requires at least a preliminary transformation of the data to get stationarity. On...
Conjugate distributions in hierarchical Bayesian ANOVA for computational efficiency and assessments of both practical and statistical significance
ANOVA xed eects random eects variance components hierar-chical Bayes multilevel model constraints
2013/4/27
Assessing variability according to distinct factors in data is a fundamental technique of statistics. The method commonly regarded to as analysis of variance (ANOVA) is, however, typically confined to...