title: Stepwise mutation likelihood computation by sequential importance sampling in subdivided population models.
abstract: 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.
authors.affiliation: Department of Mathematics, Imperial College, UK.
authors.forename: Maria
authors.initials: M
authors.lastname: De Iorio
authors.forename: Robert C
authors.initials: RC
authors.lastname: Griffiths
authors.forename: Raphael
authors.initials: R
authors.lastname: Leblois
authors.forename: François
authors.initials: F
authors.lastname: Rousset
mesh_terms: D000465:Algorithms; D025041:Animal Migration; D000818:Animals; D003198:Computer Simulation; D005589:Foxes / Q000235:genetics; D005828:Genetics, Population; D016013:Likelihood Functions; D018895:Microsatellite Repeats; D008957:Models, Genetic; D009154:Mutation