Fearnhead, Paul and Taylor, Benjamin M. (2013) An adaptive sequential Monte Carlo sampler. Bayesian Analysis, 8 (2). pp. 411-438.
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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.
|Journal or Publication Title:||Bayesian Analysis|
|Additional Information:||© 2013 International Society for Bayesian Analysis|
|Uncontrolled Keywords:||Adaptive MCMC ; Adaptive Sequential Monte Carlo ; Bayesian Mixture Analysis ; Optimal Scaling ; Stochastic Optimization|
|Subjects:||Q Science > QA Mathematics|
|Departments:||Faculty of Science and Technology > Mathematics and Statistics|
Faculty of Health and Medicine > Medicine
|Deposited By:||Mr Benjamin Taylor|
|Deposited On:||07 May 2010 13:23|
|Last Modified:||09 Oct 2013 15:39|
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