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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.
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https://hal.inrae.fr/hal-02929784
Contributor : Raphael Leblois <>
Submitted on : Thursday, September 3, 2020 - 5:58:02 PM
Last modification on : Friday, February 5, 2021 - 3:50:38 AM

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Maria de Iorio, Robert Griffiths, Raphaël Leblois, François Rousset. Stepwise mutation likelihood computation by sequential importance sampling in subdivided population models. Theoretical Population Biology, Elsevier, 2005, 68 (1), pp.41-53. ⟨10.1016/j.tpb.2005.02.001⟩. ⟨hal-02929784⟩

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