On the use of backward simulation in particle Markov chain Monte Carlo methods

Details On the use of backward simulation in particle Markov chain Monte Carlo methods On the use of backward simulation in particle Markov chain Monte Carlo methods

2011-10-13

arxiv.org/abs/1110.2873v2

Statistics

Computation

Scientific paper

Recently, Andrieu, Doucet and Holenstein (2010) introduced a general framework for using particle filters (PFs) to construct proposal kernels for Markov chain Monte Carlo (MCMC) methods. This framework, termed Particle Markov chain Monte Carlo (PMCMC), was shown to provide powerful methods for joint Bayesian state and parameter inference in nonlinear/non-Gaussian state-space models. However, the mixing of the resulting MCMC kernels can be quite sensitive, both to the number of particles used in the underlying PF and to the number of observations in the data. In the discussion following (Andrieu et al., 2010), Whiteley suggested a modified version of one of the PMCMC samplers, namely the particle Gibbs (PG) sampler, and argued that this should improve its mixing. In this paper we explore the consequences of this modification and show that it leads to a method which is much more robust to a low number of particles as well as a large number of observations. Furthermore, we discuss how the modified PG sampler can be used as a basis for alternatives to all three PMCMC samplers derived in (Andrieu et al., 2010). We evaluate these methods on several challenging inference problems in a simulation study. One of these is the identification of an epidemiological model for predicting influenza epidemics, based on search engine query data.

Affiliated with

Also associated with

No associations

Rating

On the use of backward simulation in particle Markov chain Monte Carlo methods does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with On the use of backward simulation in particle Markov chain Monte Carlo methods, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and On the use of backward simulation in particle Markov chain Monte Carlo methods will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-499982