Exact phylodynamic likelihood via structured Markov genealogy processes
Abstract
We show that each member of a broad class of Markovian population models induces a unique stochastic process on the space of genealogies. We construct this genealogy process and derive exact expressions for the likelihood of an observed genealogy in terms of a filter equation, the structure of which is completely determined by the population model. We show that existing phylodynamic methods based on the coalescent and linear birth-death processes are special cases. We derive some properties of filter equations and describe a class of algorithms that can be used to numerically solve them. Importantly, because these algorithms rely only on simulation of the population model, they retain the plug-and-play property upon which simulation-based inference depends. Our results open the door to statistically efficient likelihood-based phylodynamic inference for a much wider class of models than is currently possible.
Cite
@article{arxiv.2405.17032,
title = {Exact phylodynamic likelihood via structured Markov genealogy processes},
author = {Aaron A. King and Qianying Lin and Edward L. Ionides},
journal= {arXiv preprint arXiv:2405.17032},
year = {2026}
}