Computational inference beyond Kingman’s coalescent

Computational inference beyond Kingman’s coalescent
Jere Koskela, Paul Jenkins, Dario Spano
(Submitted on 22 Nov 2013)

Full likelihood inference under Kingman’s coalescent is a computationally challenging problem to which importance sampling (IS) and the product of approximate conditionals (PAC) method 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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