June 2015 Computational inference beyond Kingman's coalescent
Jere Koskela, Paul Jenkins, Dario Spanò
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J. Appl. Probab. 52(2): 519-537 (June 2015). DOI: 10.1239/jap/1437658613

Abstract

Full likelihood inference under Kingman's coalescent is a computationally challenging problem to which importance sampling (IS) and the product of approximate conditionals (PAC) methods have been applied successfully. Both methods can be expressed in terms of families of intractable conditional sampling distributions (CSDs), and rely on principled approximations for accurate inference. Recently, more general Λ- and Ξ-coalescents have been observed to provide better modelling fits to some genetic data sets. We derive families of approximate CSDs for finite sites Λ- and Ξ-coalescents, and use them to obtain 'approximately optimal' IS and PAC algorithms for Λ-coalescents, yielding substantial gains in efficiency over existing methods.

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Jere Koskela. Paul Jenkins. Dario Spanò. "Computational inference beyond Kingman's coalescent." J. Appl. Probab. 52 (2) 519 - 537, June 2015. https://doi.org/10.1239/jap/1437658613

Information

Published: June 2015
First available in Project Euclid: 23 July 2015

zbMATH: 1347.60120
MathSciNet: MR3372090
Digital Object Identifier: 10.1239/jap/1437658613

Subjects:
Primary: 60G09
Secondary: 92D25 , 93E10

Keywords: conditional sampling distribution , importance sampling , Lambda-coalescent , Population genetics , product of approximate conditionals , Xi-coalescent

Rights: Copyright © 2015 Applied Probability Trust

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Vol.52 • No. 2 • June 2015
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