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. Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.
Citation
Aaron A King, Qianying Lin, Edward L Ionides. Markov genealogy processes. Theoretical population biology. 2022 Feb;143:77-91
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PMID: 34896438
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