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  • Title: Markov chain Monte Carlo without likelihoods.
    Author: Marjoram P, Molitor J, Plagnol V, Tavare S.
    Journal: Proc Natl Acad Sci U S A; 2003 Dec 23; 100(26):15324-8. PubMed ID: 14663152.
    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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