Approximate Bayesian Computation and MCMC

Vincent Plagnol () and Simon Tavaré ()
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Vincent Plagnol: University of Southern California, Program in Molecular and Computational Biology
Simon Tavaré: University of Southern California, Program in Molecular and Computational Biology

A chapter in Monte Carlo and Quasi-Monte Carlo Methods 2002, 2004, pp 99-113 from Springer

Abstract: Summary For many complex probability models, computation of likelihoods is either impossible or very time consuming. In this article, we discuss methods for simulating observations from posterior distributions without the use of likelihoods. A rejection approach is illustrated using an example concerning inference in the fossil record. A novel Markov chain Monte Carlo approach is also described, and illustrated with an example from population genetics.

Keywords: Posterior Distribution; Fossil Record; MCMC Method; Approximate Bayesian Computation; Much Recent Common Ancestor (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-18743-8_5

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DOI: 10.1007/978-3-642-18743-8_5

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