On particle Gibbs sampling

Abstract

The particle Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm to sample from the full posterior distribution of a state-space model. It does so by executing Gibbs sampling steps on an extended target distribution defined on the space of the auxiliary variables generated by an interacting particle system. This paper makes the following contributions to the theoretical study of this algorithm. Firstly, we present a coupling construction between two particle Gibbs updates from different starting points and we show that the coupling probability may be made arbitrarily close to one by increasing the number of particles. We obtain as a direct corollary that the particle Gibbs kernel is uniformly ergodic. Secondly, we show how the inclusion of an additional Gibbs sampling step that reselects the ancestors of the particle Gibbs' extended target distribution, which is a popular approach in practice to improve mixing, does indeed yield a theoretically more efficient algorithm as measured by the asymptotic variance. Thirdly, we extend particle Gibbs to work with lower variance resampling schemes. A detailed numerical study is provided to demonstrate the efficiency of particle Gibbs and the proposed variants.

Published at http://dx.doi.org/10.3150/14-BEJ629 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)

References in corpus (4)

• Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms
• Path storage in the particle filter
• Particle filtering within adaptive Metropolis Hastings sampling
• On particle filters applied to electricity load forecasting

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• Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlo
• Bayesian learning of weakly structural Markov graph laws using sequential Monte Carlo methods
• A Point Mass Proposal Method for Bayesian State-Space Model Fitting