An adaptive sequential Monte Carlo method for approximate Bayesian computation

Pierre Del Moral, Arnaud Doucet, Ajay Jasra

Research output: Contribution to journalArticlepeer-review

247 Scopus citations


Approximate Bayesian computation (ABC) is a popular approach to address inference problems where the likelihood function is intractable, or expensive to calculate. To improve over Markov chain Monte Carlo (MCMC) implementations of ABC, the use of sequential Monte Carlo (SMC) methods has recently been suggested. Most effective SMC algorithms that are currently available for ABC have a computational complexity that is quadratic in the number of Monte Carlo samples (Beaumont et al., Biometrika 86:983-990, 2009; Peters et al., Technical report, 2008; Toni et al., J. Roy. Soc. Interface 6:187-202, 2009) and require the careful choice of simulation parameters. In this article an adaptive SMC algorithm is proposed which admits a computational complexity that is linear in the number of samples and adaptively determines the simulation parameters. We demonstrate our algorithm on a toy example and on a birth-death-mutation model arising in epidemiology. © 2011 Springer Science+Business Media, LLC.
Original languageEnglish (US)
JournalStatistics and Computing
Issue number5
StatePublished - Sep 1 2012
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2019-11-20


Dive into the research topics of 'An adaptive sequential Monte Carlo method for approximate Bayesian computation'. Together they form a unique fingerprint.

Cite this