Please use this identifier to cite or link to this item:
Title: An adaptive sequential Monte Carlo method for approximate Bayesian computation
Authors: Del Moral, P.
Doucet, A.
Jasra, A. 
Keywords: Approximate Bayesian computation
Markov chain Monte Carlo
Sequential Monte Carlo
Issue Date: Sep-2012
Citation: Del Moral, P., Doucet, A., Jasra, A. (2012-09). An adaptive sequential Monte Carlo method for approximate Bayesian computation. Statistics and Computing 22 (5) : 1009-1020. ScholarBank@NUS Repository.
Abstract: 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.
Source Title: Statistics and Computing
ISSN: 09603174
DOI: 10.1007/s11222-011-9271-y
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.