Markov genealogy processes

Aaron A. King, Qianying Lin and Edward L. Ionides

Theoretical Population Biology, 2022, vol. 143, issue C, 77-91

Abstract: We construct a family of genealogy-valued Markov processes that are induced by a continuous-time Markov population process. We derive exact expressions for the likelihood of a given genealogy conditional on the history of the underlying population process. These lead to a nonlinear filtering equation which can be used to design efficient Monte Carlo inference algorithms. We demonstrate these calculations with several examples. Existing full-information approaches for phylodynamic inference are special cases of the theory.

Keywords: Phylodynamics; Partially observed Markov process; Hidden Markov model; Statistical inference; Molecular epidemiology; Phylogeny (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:thpobi:v:143:y:2022:i:c:p:77-91

DOI: 10.1016/j.tpb.2021.11.003

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