A search for Markov chain Monte Carlo (or MCMC) articles on Google Scholar yields over 100,000 hits, and a general web search on Google yields 1.7 million hits. These results stem largely from the ubiquitous use of these algorithms in modern computational statistics, as we shall now describe. MCMC algorithms are used to solve problems in many scientific fields, including physics (where many MCM...

Isolation with Migration model (IM), which jointly estimates divergence times and migration rates between two populations from DNA sequence data, can capture many phenomena when one population splits into two. The parameters inferences for IM are based on Markov Chain Monte Carlo method (MCMC). Standard implementations of MCMC are prone to fall into local optima. Metropolis Coupled MCMC [(MC)3]...

Markov Chain Monte Carlo methods are statistical tools that have been recently proposed for the resolution of the MEG inverse problem [1]. Their main advantages are easy incorporation of a priori knowledge, and an adequate response to the ambiguity of the ill-posed MEG inverse problem. However, since simpler MCMC schemes might have difficulties in escaping from local modes, adequate research is...

An R package mixAK is introduced which implements routines for a semiparametric density estimation through normal mixtures using the Markov chain Monte Carlo (MCMC) methodology. Besides producing the MCMC output, the package computes posterior summary statistics for important characteristics of the fitted distribution or computes and visualizes the posterior predictive density. For the estimate...

Abstract The hierarchical Dirichlet process (HDP) has become an important Bayesian nonparametric model for grouped data, such as document collections. The HDP is used to construct a flexible mixed-membership model where the number of components is determined by the data. As for most Bayesian nonparametric models, exact posterior inference is intractable—practitioners use Markov chain Monte Carl...

Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling are finding widespread use in applied statistics and machine learning. These often require significant computational power, and are increasingly being deployed on parallel and distributed systems such as compute clusters. Recent work has proposed running iterative algorithms such as gradient descent and MCMC in parallel asynchronous...

As it has become common to use many computer cores in routine applications, finding good ways to parallelize popular algorithms has become increasingly important. In this paper, we present a parallelization scheme for Markov chain Monte Carlo (MCMC) methods based on spectral clustering of the underlying state space, generalizing earlier work on parallelization of MCMC methods by state space par...

A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete possible worlds underlying any given knowledge base. The simplicity of the approach and its lazy construction of possible worlds make it possible to consider quite expressive languages. In particular, we consider two ext...

This paper presents a new glottal inverse filtering (GIF) method that utilizes Markov chain Monte Carlo (MCMC) algorithm. First, initial estimates of the vocal tract and glottal flow are evaluated by an existing GIF method, the iterative adaptive inverse filtering (IAIF). Simultaneously, the initially estimated glottal flow is synthesized using the Klatt model and filtered with the estimated vo...

In this paper we present a novel approach for human Body configuration based on the Silhouette. We propose to address this problem under the Bayesian framework. We use an effective Model based MCMC (Markov Chain Monte Carlo) method to solve the configuration problem, in which the best configuration could be defined as MAP (maximize a posteriori probability) in Bayesian model. This model based M...