The problem of sampling from a given distribution on high-dimensional continuous spaces arises in the computational sciences and Bayesian statistics, and a frequentlyused solution is Markov chain Monte Carlo (MCMC); see [13] for many examples. Because MCMC methods produce good samples only after a lengthy mixing period, a long-standing mathematical question is to analyze the mixing times of the...

We introduce a hierarchical Bayesian model for the discovery of putative regulators from gene expression data only. The hierarchy incorporates the knowledge that only a few regulators regulate most of the genes. This is implemented through a spike-and-slab prior with mixing weights from a hierarchical Bernoulli model. For efficient inference we implemented expectation propagation. Running the m...

the objective of the present study was to estimate heritability values for some performance and egg quality traits of native fowl in isfahan breeding center using reml and bayesian approaches. the records were about 51521 and 975 for performance and egg quality traits, respectively. at the first step, variance components were estimated for body weight at hatch (bw0), body weight at 8 weeks of a...

In this article we demonstrate how to generate independent and identically distributed samples from the model space of the Bayes linear model with orthogonal predictors. We use the method of coupled Markov chains from the past as introduced by Propp and Wilson (1996). This procedure alleviates any concerns over convergence and sample mixing. We present a number of examples including a perfect s...

We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only signi cant lags are included. Joint sampling of the indicators and parameters is found to speed convergence...

We introduce a hierarchical Bayesian model for the discovery of putative regulators from gene expression data only. The hierarchy incorporates the knowledge that there are just a few regulators that by themselves only regulate a handful of genes. This is implemented through a so-called spike-and-slab prior, a mixture of Gaussians with different widths, with mixing weights from a hierarchical Be...

Hyperspectral images can be represented either as a set of images or as a set of spectra. Spectral classification and segmentation and data reduction are the main problems in hyperspectral image analysis. In this paper we propose a Bayesian estimation approach with an appropriate hiearchical model with hidden markovian variables which gives the possibility to jointly do data reduction, spectral...

We develop a Bayesian nonparametric mixture modeling framework for replicated count responses in dose-response settings. We explore this methodology for modeling and risk assessment in developmental toxicity studies, where the primary objective is to determine the relationship between the level of exposure to a toxic chemical and the probability of a physiological or biochemical response, or de...

This paper introduces the Bayesian Inference Engine (BIE), a general parallel-optimised software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organise and reuse expensive derived data. I describe key concepts that illustrate the power of Bayesian inference to address these needs and outline th...