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Activity Number: 309
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #307698
Title: Empirically Comparing the Performance of Local MCMC Algorithms with Pools of Proposals
Author(s): Mylène Bédard*+
Companies: Université De Montréal
Keywords: computational cost ; delayed rejection ; Metropolis-Hastings ; multiple-try ; regression

To improve on the Metropolis algorithm, various MCMC methods with pools of proposals, such as the multiple-try and delayed rejection Metropolis algorithms, have been proposed. These methods generate several candidates in a single iteration; accordingly they are computationally more intensive than the Metropolis algorithm. It is usually difficult to provide a general estimate for the computational cost of a method without being overly conservative; potentially efficient methods could thus be overlooked by relying on such estimates. In this talk, we describe three algorithms with pools of proposals - the multiple-try Metropolis (MTM) algorithm, the MTM hit-and-run algorithm, and the delayed rejection Metropolis algorithm with antithetic proposals (DR-A) - and investigate the net performance of these samplers in various contexts. To allow for a fair comparison, the study is carried under optimal mixing conditions for each of these samplers. The algorithms are used in the contexts of Bayesian logistic regressions and classical inference for a linear regression model. This talk is based on work in collaboration with M. Mireuta, E. Moulines, and R. Douc.

Authors who are presenting talks have a * after their name.

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