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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
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 | ||||
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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: |
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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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