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An adaptive sequential Monte Carlo sampler

Research output: Contribution to journalJournal article

Published

Journal publication date2013
JournalBayesian Analysis
Journal number2
Volume8
Number of pages28
Pages411-438
Early online date6/03/13
Original languageEnglish

Abstract

Sequential Monte Carlo (SMC) methods are not only a popular tool
in the analysis of state–space models, but offer an alternative to Markov chain
Monte Carlo (MCMC) in situations where Bayesian inference must proceed via
simulation. This paper introduces a new SMC method that uses adaptive MCMC kernels for particle dynamics. The proposed algorithm features an online stochastic optimization procedure to select the best MCMC kernel and simultaneously learn optimal tuning parameters. Theoretical results are presented that justify the approach and give guidance on how it should be implemented. Empirical results, based on analysing data from mixture models, show that the new adaptive SMC algorithm (ASMC) can both choose the best MCMC kernel, and learn an appropriate scaling for it. ASMC with a choice between kernels outperformed the adaptive MCMC algorithm of Haario et al. (1998) in 5 out of the 6 cases considered.

Bibliographic note

© 2013 International Society for Bayesian Analysis