An Adaptive Sequential Monte Carlo Sampler

By Paul Fearnhead, Benjamin M. Taylor. 2010

85 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Kernel Sequential Monte Carlo (2015, methodology): “In this paper, we focus on static SMC methods, which recently have generated increasing interest as an alternative to MCMC for Bayesian inference on a single target distribution [3, 4, 6, 11].”
Towards Automatic Model Comparison: An Adaptive Sequential Monte Carlo Approach (2013, methodology): “There are many examples of such an annealed SMC strategy outperforming MCMC at a given computational cost — see, for example, Fan et al. (2008); Johansen et al. (2008); Fearnhead and Taylor (2010).”