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EXACT PHYLODYNAMIC LIKELIHOOD VIA STRUCTURED MARKOV GENEALOGY PROCESSES.

Aaron A King1, Qianying Lin2, Edward L Ionides3

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We developed a method to infer population structures using geneaology data from Markov population processes. This allows for efficient statistical inference in complex compartment models.

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Area of Science:

  • Population genetics
  • Stochastic processes
  • Computational biology

Background:

  • Markov population processes model evolving populations with distinct states.
  • Genealogies represent the ancestral relationships of sampled individuals.
  • Statistical exchangeability within compartments simplifies population modeling.

Purpose of the Study:

  • To construct the time-evolving genealogy process for Markov population models.
  • To derive exact likelihood expressions for observed genealogies.
  • To enable efficient statistical inference for compartment models using genealogy data.

Main Methods:

  • Construction of the genealogy process for discrete compartment models.
  • Derivation of likelihood expressions using filter equations.
  • Numerical solution of filter equations via Monte Carlo integration.

Main Results:

  • Statistically efficient likelihood-based inference is achieved.
  • The method applies to arbitrary compartment models.
  • Inference is based on observed genealogies of sampled individuals.

Conclusions:

  • The developed framework provides a powerful tool for analyzing population structures.
  • This approach enhances our ability to infer complex demographic histories from genetic data.
  • Efficient computational methods allow for practical application in population genetics research.