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.
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Plagnol, V., Tavaré, S. (2004). Approximate Bayesian Computation and MCMC. In: Niederreiter, H. (eds) Monte Carlo and Quasi-Monte Carlo Methods 2002. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18743-8_5
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DOI: https://doi.org/10.1007/978-3-642-18743-8_5
Publisher Name: Springer, Berlin, Heidelberg
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