Markov chain Monte Carlo without likelihoods
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 maximumlikelihood estimation. The approach is illustrated by an example of ancestral inference in population genetics. A number of open problems are highlighted in the discussion.
 Publication:

Proceedings of the National Academy of Science
 Pub Date:
 December 2003
 DOI:
 10.1073/pnas.0306899100
 Bibcode:
 2003PNAS..10015324M
 Keywords:

 STATISTICS