Both social and engineering factors must be further explored and scrutinized across the globe—as should notions of justice related to flooding impacts and responses.
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Communicated by Michael S. Waterman, University of Southern California, Los Angeles, CA, October 24, 2003 (received for review June 20, 2003)
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.
Footnotes
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↵‡ To whom correspondence should be addressed at: Program in Molecular and Computational Biology, Department of Biological Sciences, SHS 172, University of Southern California, 835 West 37th Street, Los Angeles, CA 90089-1340. E-mail: stavare{at}usc.edu.
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Abbreviations: MCMC, Markov chain Monte Carlo; MRCA, most recent common ancestor.
- Received June 20, 2003.
- Copyright © 2003, The National Academy of Sciences
Markov chain Monte Carlo without likelihoods
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