Title of article :
On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
Author/Authors :
Pitt، نويسنده , , Michael K. and Silva، نويسنده , , Ralph dos Santos and Giordani، نويسنده , , Paolo and Kohn، نويسنده , , Robert، نويسنده ,
Issue Information :
دوفصلنامه با شماره پیاپی سال 2012
Abstract :
Andrieu et al. (2010) prove that 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 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 the auxiliary particle filter and use the framework of Del Moral (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.
Keywords :
Bayesian inference , Simulated likelihood , Auxiliary variables , Adapted filtering
Journal title :
Journal of Econometrics
Journal title :
Journal of Econometrics