Tasks such as record linkage and multi-target tracking, which involve reconstructing the set of objects that underlie some observed data, are particularly challenging for probabilistic inference. Recent work has achieved efficient and accurate inference on such problems using Markov chain Monte Carlo (MCMC) techniques with customized proposal distributions. Currently, implementing such a system...

Bayes Nets simplify probabilistic models, making it easy to work with these models. Unfortunately, sometimes people devise models that are too complicated to allow calculation of exact probabilities, so they instead use approximate inference, such as Markov Chain Monte Carlo (MCMC). However, MCMC can fail if the Bayes Net has zero-probability states that “disconnect” the state space. In this pa...

A general methodology is presented for the construction and effective use of control variates for reversible MCMC samplers. The values of the coefficients of the optimal linear combination of the control variates are computed, and adaptive, consistent MCMC estimators are derived for these optimal coefficients. All methodological and asymptotic arguments are rigorously justified. Numerous MCMC s...

This paper considers the implementation of efficient Bayesian computation for large linear Gaussian models containing many latent variables. A common approach is to implement a simple MCMC procedure such as the Gibbs sampler or data augmentation, but these methods are often unsatisfactory when the model is large. This motivates the need to develop other strategies for improving MCMC. This paper...