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- Title
COMPUTATIONAL INFERENCE BEYOND KINGMAN'S COALESCENT.
- Authors
KOSKELA, JERE; JENKINS, PAUL; SPANÒ, DARIO
- 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 ...-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 ...-coalescents, and use them to obtain 'approximately optimal' IS and PAC algorithms for Λ-coalescents, yielding substantial gains in efficiency over existing methods.
- Subjects
COMPUTATIONAL complexity; INFERENTIAL statistics; PROBLEM solving; APPROXIMATION theory; MATHEMATICAL proofs
- Publication
Journal of Applied Probability, 2015, Vol 52, Issue 2, p519
- ISSN
0021-9002
- Publication type
Article
- DOI
10.1239/jap/1437658613