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Stepwise mutation likelihood computation by sequential importance sampling in subdivided population models
Journal article   Peer reviewed

Stepwise mutation likelihood computation by sequential importance sampling in subdivided population models

M. de Iorio, R. Griffiths, Raphaël Leblois and Francois Rousset
Theoretical Population Biology, Vol.68(1), pp.41-53
07/2005

Abstract

Coalescent process Importance sampling Migration Stepwise mutation
An importance sampling algorithm for computing the likelihood of a sample of genes at loci under a stepwise mutation model in a subdivided population is developed. This allows maximum likelihood estimation of migration rates between subpopulations. The time to the most recent common ancestor of the sample can also be computed. The technique is illustrated by an analysis of a data set of Australian red fox populations.
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