An adaptive sequential Monte Carlo sampler

Fearnhead, Paul and Taylor, Benjamin M. (2013) An adaptive sequential Monte Carlo sampler. Bayesian Analysis, 8 (2). pp. 411-438. ISSN 1931-6690

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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.

Item Type:
Journal Article
Journal or Publication Title:
Bayesian Analysis
Additional Information:
© 2013 International Society for Bayesian Analysis
Uncontrolled Keywords:
?? adaptive mcmcadaptive sequential monte carlobayesian mixture analysisoptimal scalingstochastic optimizationstatistics and probabilityapplied mathematicsqa mathematics ??
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Deposited On:
07 May 2010 12:23
Last Modified:
09 Apr 2024 23:38