Journal of Applied Probability

Computational inference beyond Kingman's coalescent

Jere Koskela, Paul Jenkins, and Dario Spanò

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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.

Article information

J. Appl. Probab., Volume 52, Number 2 (2015), 519-537.

First available in Project Euclid: 23 July 2015

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 60G09: Exchangeability
Secondary: 93E10: Estimation and detection [See also 60G35] 92D25: Population dynamics (general)

Lambda-coalescent xi-coalescent product of approximate conditionals importance sampling conditional sampling distribution population genetics


Koskela, Jere; Jenkins, Paul; Spanò, Dario. Computational inference beyond Kingman's coalescent. J. Appl. Probab. 52 (2015), no. 2, 519--537. doi:10.1239/jap/1437658613.

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