Markov genealogy processes
Copyright policy )Markov genealogy processes
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.
- University of Michigan–Ann Arbor United States
- University of Michigan United States
- University of Michigan–Flint United States
- Regents of the University of Michigan - Ann Arbor United States
FOS: Computer and information sciences, phylogeny, molecular epidemiology, Quantitative Biology - Quantitative Methods, Statistics - Applications, partially observed Markov process, Methodology (stat.ME), Problems related to evolution, FOS: Mathematics, Applications (stat.AP), Quantitative Biology - Populations and Evolution, hidden Markov model, Statistics - Methodology, Quantitative Methods (q-bio.QM), Probability (math.PR), Populations and Evolution (q-bio.PE), Bayes Theorem, phylodynamics, Markov Chains, FOS: Biological sciences, Applications of continuous-time Markov processes on discrete state spaces, 60J99, Monte Carlo Method, Mathematics - Probability, Algorithms, statistical inference
FOS: Computer and information sciences, phylogeny, molecular epidemiology, Quantitative Biology - Quantitative Methods, Statistics - Applications, partially observed Markov process, Methodology (stat.ME), Problems related to evolution, FOS: Mathematics, Applications (stat.AP), Quantitative Biology - Populations and Evolution, hidden Markov model, Statistics - Methodology, Quantitative Methods (q-bio.QM), Probability (math.PR), Populations and Evolution (q-bio.PE), Bayes Theorem, phylodynamics, Markov Chains, FOS: Biological sciences, Applications of continuous-time Markov processes on discrete state spaces, 60J99, Monte Carlo Method, Mathematics - Probability, Algorithms, statistical inference
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