On Sequential Monte Carlo, Partial Rejection Control and Approximate Bayesian Computation

Probability Surveys, ISSN: 1549-5787 (2008)

24 Pages Posted: 5 Jun 2017

See all articles by Gareth Peters

Gareth Peters

University of California Santa Barbara; affiliation not provided to SSRN

Y. Fan

University of New South Wales (UNSW) - School of Mathematics and Statistics

Scott Sisson

University of New South Wales (UNSW) - School of Mathematics and Statistics

Date Written: 2008

Abstract

We present a sequential Monte Carlo sampler variant of the partial rejection control algorithm introduced by Liu (2001), termed SMC sampler PRC, and show that this variant can be considered under the same framework of the sequential Monte Carlo sampler of Del Moral et al. (2006). We make connections with existing algorithms and theoretical results, and extend some theoretical results to the SMC sampler PRC setting. We examine the properties of the SMC sampler PRC and give recommendations for user specified quantities. We also study the special case of SMC sampler PRC in the “likelihood free” approximate Bayesian computation framework, as introduced by Sisson et al. (2007).

Keywords: Asymptotic analysis, Convergence, Interacting particle systems, Sequential Monte Carlo samplers

Suggested Citation

Peters, Gareth and Fan, Y. and Sisson, Scott, On Sequential Monte Carlo, Partial Rejection Control and Approximate Bayesian Computation (2008). Probability Surveys, ISSN: 1549-5787 (2008), Available at SSRN: https://ssrn.com/abstract=2980448

Gareth Peters (Contact Author)

University of California Santa Barbara ( email )

Santa Barbara, CA 93106
United States

affiliation not provided to SSRN

Y. Fan

University of New South Wales (UNSW) - School of Mathematics and Statistics ( email )

Sydney, 2052
Australia

Scott Sisson

University of New South Wales (UNSW) - School of Mathematics and Statistics ( email )

Sydney, 2052
Australia

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