Markov chain Monte Carlo (MCMC) routines have become a fundamental means for generating random variates from distributions otherwise difficult to sample. The Hastings sampler, which includes the Gibbs and Metropolis samplers as special cases, is the most popular MCMC method. A number of implementations are available for running these MCMC routines varying in the order through which the componen...

With the rapidly growing scales of statistical problems, subset based communicationfree parallel MCMC methods are a promising future for large scale Bayesian analysis. In this article, we propose a new Weierstrass sampler for parallel MCMC based on independent subsets. The new sampler approximates the full data posterior samples via combining the posterior draws from independent subset MCMC cha...

Conditional random fields and other graphical models have achieved state of the art results in a variety of tasks such as coreference, relation extraction, data integration, and parsing. Increasingly, practitioners are using models with more complex structure—higher treewidth, larger fan-out, more features, and more data—rendering even approximate inference methods such as MCMC inefficient. In ...

A key challenge for cognitive psychology is the investigation of mental representations, such as object categories, subjective probabilities, choice utilities, and memory traces. In many cases, these representations can be expressed as a non-negative function defined over a set of objects. We present a behavioral method for estimating these functions. Our approach uses people as components of a...

Conditional random fields and other graphical models have achieved state of the art results in a variety of NLP and IE tasks including coreference and relation extraction. Increasingly, practitioners are using models with more complex structure—higher tree-width, larger fanout, more features, and more data—rendering even approximate inference methods such as MCMC inefficient. In this paper we p...

The use of Bayesian inference for the analysis of complex statistical models has increased dramatically in recent years, in part due to the increasing availability of computing power. There are a range of techniques available for carrying out Bayesian inference, but the lack of analytic tractability for the vast majority of models of interest means that most of the techniques are numeric, and m...

We study inference for parameters defined by either classical extremum estimators or Laplace-type subject to general nonlinear constraints on the parameters. show that running MCMC penalized version of problem offers a computationally attractive alternative solving original constrained optimization problem. Bayesian credible intervals are asymptotically valid confidence in pointwise sense, prov...

Abstract Efficient sampling of many-dimensional and multimodal density functions is a task great interest in many research fields. We describe an algorithm that allows parallelizing inherently serial Markov chain Monte Carlo (MCMC) by partitioning the space function parameters into multiple subspaces each them independently. The samples different are then reweighted their integral values stitch...