Markov chain Monte Carlo without likelihoods

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...

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Bibliographic Details
Main Authors: Marjoram, Paul, Molitor, John, Plagnol, Vincent, Tavaré, Simon
Format: Artigo
Language:Inglês
Published: National Academy of Sciences 2003
Subjects:
Physical Sciences
Online Access:https://ncbi.nlm.nih.gov/pmc/articles/PMC307566/
https://ncbi.nlm.nih.gov/pubmed/14663152
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.0306899100
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