Statistics and Computing

, Volume 22, Issue 5, pp 1009–1020

An adaptive sequential Monte Carlo method for approximate Bayesian computation


DOI: 10.1007/s11222-011-9271-y

Cite this article as:
Del Moral, P., Doucet, A. & Jasra, A. Stat Comput (2012) 22: 1009. doi:10.1007/s11222-011-9271-y


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.


Approximate Bayesian computation Markov chain Monte Carlo Sequential Monte Carlo 

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Centre INRIA Bordeaux Sud-Ouest & Institut de MathématiquesUniversité Bordeaux ITalence cedexFrance
  2. 2.Department of StatisticsUniversity of OxfordOxfordUK
  3. 3.Department of Statistics and Applied ProbabilityNational University of SingaporeSingaporeSingapore

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