Send to:

Choose Destination
See comment in PubMed Commons below
Proc Natl Acad Sci U S A. 2003 Dec 23;100(26):15324-8. Epub 2003 Dec 8.

Markov chain Monte Carlo without likelihoods.

Author information

  • 1Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA.


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.

[PubMed - indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for HighWire Icon for PubMed Central
    Loading ...
    Write to the Help Desk