On Some Properties of Markov Chain Monte Carlo Simulation Methods Based on the Particle Filter

• Activity Number: 312
• Type: Invited
• Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Council of Chapters
• Author(s): Robert Jacob Kohn*+ and Michael Pitt and Paolo Giordani
• Companies: University of New South Wales and University of Warwick and Sveriges Riksbank
• Keywords: Auxiliary variables ; Adapted filtering ; Bayesian inference

Abstract

Markov chain Monte Carlo samplers still converge to the correct posterior distribution of the model parameters when the likelihood estimated by the particle filter (with a finite number of particles) is used instead of the true likelihood. A critical issue for performance is the choice of the number of particles. We add the following contributions. First, we provide analytically derived, practical guidelines on the optimal number of particles to use. Second, we show that a fully adapted auxiliary particle filter is unbiased and can drastically decrease computing time compared to a standard particle filter. Third, we introduce a new estimator of the likelihood based on the output of a general auxiliary particle filter and use the framework of delmoral( 2004} to provide a direct proof of the unbiasedness of the estimator. Fourth, we show that the results in the article apply more generally to Markov chain Monte Carlo sampling schemes with the likelihood estimated in an unbiased manner.