Markov chain Monte Carlo without likelihoods.

Paul Marjoram; John Molitor; Vincent Plagnol; Simon Tavare

doi:10.1073/pnas.0306899100

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Markov chain Monte Carlo without likelihoods.

Paul Marjoram·John Molitor·Vincent Plagnol·Simon Tavare

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Abstract

Many stochastic simulation approaches for generating observations from a posterior distribution depend on knowing a likelihood function. However, for many complex probability models, such likelihoods are either impossible or computationally prohibitive to obtain. Here we present a Markov chain Monte Carlo method for generating observations from a posterior distribution without the use of likelihoods. It can also be used in frequentist applications, in particular for maximum-likelihood estimation. The approach is illustrated by an example of ancestral inference in population genetics. A number of open problems are highlighted in the discussion.

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Algorithms Biological Evolution Computer Simulation DNA DNA, Mitochondrial Genetics, Population Humans Likelihood Functions Markov Chains Models, Biological Monte Carlo Method Stochastic Processes Journal Article Research Support, U.S. Gov't, P.H.S.

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Paul Marjoram, John Molitor, Vincent Plagnol, Simon Tavare. 2003-12-08. Markov chain Monte Carlo without likelihoods.. https://doi.org/10.1073/pnas.0306899100

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