Exact phylodynamic likelihood via structured Markov genealogy processes
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918514968756224 |
|---|---|
| author | King, Aaron A. Lin, Qianying Ionides, Edward L. |
| author_facet | King, Aaron A. Lin, Qianying Ionides, Edward L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17032 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exact phylodynamic likelihood via structured Markov genealogy processes King, Aaron A. Lin, Qianying Ionides, Edward L. Quantitative Methods Probability Populations and Evolution Applications 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. |
| title | Exact phylodynamic likelihood via structured Markov genealogy processes |
| topic | Quantitative Methods Probability Populations and Evolution Applications |
| url | https://arxiv.org/abs/2405.17032 |