UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Markov chain Monte Carlo without likelihoods.

Marjoram, P; Molitor, J; Plagnol, V; Tavare, S; (2003) Markov chain Monte Carlo without likelihoods. Proc Natl Acad Sci U S A , 100 (26) 15324 - 15328. 10.1073/pnas.0306899100.

Full text not available from this repository.

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.

Type:Article
Title:Markov chain Monte Carlo without likelihoods.
Location:United States
DOI:10.1073/pnas.0306899100
Language:English
Additional information:PMCID: PMC307566
Keywords:Algorithms, Biological Evolution, Computer Simulation, DNA, DNA, Mitochondrial, Genetics, Population, Humans, Likelihood Functions, Markov Chains, Models, Biological, Monte Carlo Method, Stochastic Processes
UCL classification:UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Biosciences (Division of) > Genetics, Evolution and Environment > UCL Genetics Institute

Archive Staff Only: edit this record