Two coupled Gibbs sampler chains, both with invariant probability density , are run in parallel in such a way that the chains are negatively correlated. This allows us to define an estimator of the expectation E with respect to which achieves significant variance reduction with respect to the usual Gibbs sampler at comparable computational costs. We show that the asymptotic variance of the esti...

Learning a tree substitution grammar is very challenging due to derivational ambiguity. Our recent approach used a Bayesian non-parametric model to induce good derivations from treebanked input (Cohn et al., 2009), biasing towards small grammars composed of small generalisable productions. In this paper we present a novel training method for the model using a blocked Metropolis-Hastings sampler...

In this paper, we propose to monitor a Markov chain sampler using the cusum path plot of a chosen 1-dimensional summary statistic. We argue that the cusum path plot can bring out, more e ectively than the sequential plot, those aspects of a Markov sampler which tell the user how quickly or slowly the sampler is moving around in its sample space, in the direction of the summary statistic. The pr...

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...

The Gibbs Centroid Sampler is a software package designed for locating conserved elements in biopolymer sequences. The Gibbs Centroid Sampler reports a centroid alignment, i.e. an alignment that has the minimum total distance to the set of samples chosen from the a posteriori probability distribution of transcription factor binding-site alignments. In so doing, it garners information from the f...

We analyze a hierarchical Bayes model which is related to the usual empirical Bayes formulation of James-Stein estimators. We consider running a Gibbs sampler on this model. Using previous results about convergence rates of Markov chains, we provide rigorous, numerical, reasonable bounds on the running time of the Gibbs sampler, for a suitable range of prior distributions. We apply these result...

The problem of building an `0-sampler is to sample nearuniformly from the support set of a dynamic multiset. This problem has a variety of applications within data analysis, computational geometry and graph algorithms. In this paper, we abstract a set of steps for building an `0-sampler, based on sampling, recovery and selection. We analyze the implementation of an `0-sampler within this framew...

Five different water passive samplers were calibrated under laboratory conditions for measurement of 124 legacy and current used pesticides. This study provides a protocol for the passive sampler preparation, calibration, extraction method and instrumental analysis. Sampling rates (RS) and passive sampler-water partition coefficients (KPW) were calculated for silicone rubber, polar organic chem...

We present an MCMC sampler for Dirichlet process mixture models that can be parallelized to achieve significant computational gains. We combine a nonergodic, restricted Gibbs iteration with split/merge proposals in a manner that produces an ergodic Markov chain. Each cluster is augmented with two subclusters to construct likely split moves. Unlike some previous parallel samplers, the proposed s...

We introduce a new sampling algorithm, the equi-energy sampler, for efficient statistical sampling and estimation. Complementary to the widely used temperature-domain methods, the equi-energy sampler, utilizing the temperature-energy duality, targets the energy directly. The focus on the energy function not only facilitates efficient sampling, but also provides a powerful means for statistical ...