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Likelihood-free estimation of model evidence

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Didelot, Xavier, Everitt, Richard G., Johansen, Adam M. and Lawson, Daniel J. (2011) Likelihood-free estimation of model evidence. Bayesian Analysis, Vol.6 (No.1). pp. 49-76. doi:10.1214/11-BA602

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Official URL: http://dx.doi.org/10.1214/11-BA602

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Abstract

Statistical methods of inference typically require the likelihood function to be computable in a reasonable amount of time. The class of "likelihood-free" methods termed Approximate Bayesian Computation (ABC) is able to eliminate this requirement, replacing the evaluation of the likelihood with simulation from it. Likelihood-free methods have gained in efficiency and popularity in the past few years, following their integration with Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) in order to better explore the parameter space. They have been applied primarily to estimating the parameters of a given model, but can also be used to compare models.

Item Type:

Journal Article

Subjects:

Q Science > QA Mathematics

Divisions:

Faculty of Science, Engineering and Medicine > Science > Statistics

Library of Congress Subject Headings (LCSH):

Mathematical statistics, Bayesian statistical decision theory, Mathematical models

Journal or Publication Title:

Bayesian Analysis

Publisher:

International Society for Bayesian Analysis

ISSN:

1931-6690

Official Date:

2011

Dates:

Date	Event
2011	Published

Volume:

Vol.6

Number:

No.1

Page Range:

pp. 49-76

DOI:

10.1214/11-BA602

Status:

Peer Reviewed

Publication Status:

Published

Version or Related Resource:

Didelot, X., et al. (2010). Likelihood-free estimation of model evidence. [Coventry] : University of Warwick. Centre for Research in Statistical Methodology. (Working papers, no.10-12). http://wrap.warwick.ac.uk/id/eprint/35076

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