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Universal probabilistic programming offers a powerful approach to statistical phylogenetics.

Fredrik Ronquist1, Jan Kudlicka2, Viktor Senderov3

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Summary
This summary is machine-generated.

Universal probabilistic programming languages (PPLs) simplify phylogenetic analysis by enabling automated inference for complex biological diversification models. This approach facilitates model testing and reveals that models with slowing diversification best explain bird phylogeny data.

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Area of Science:

  • Evolutionary biology
  • Computational phylogenetics
  • Statistical modeling

Background:

  • Phylogenetic analysis traditionally uses complex software, limiting exploration of new evolutionary models.
  • Probabilistic graphical models offer generic solutions but have limitations in expressing phylogenetic problems.

Purpose of the Study:

  • To demonstrate that universal probabilistic programming languages (PPLs) overcome expressivity limitations in phylogenetic analysis.
  • To enable automated generation of efficient inference algorithms for PPL-based phylogenetic models.

Main Methods:

  • Developed automated generation of sequential Monte Carlo (SMC) algorithms for PPL descriptions of birth-death diversification models.
  • Applied SMC algorithms to analyze 40 bird phylogenies.

Main Results:

  • PPLs provide full expressivity for phylogenetic problems, including complex diversification models.
  • Automated SMC algorithm generation allows inference for previously intractable models.
  • Analysis of bird phylogenies indicates models with slowing diversification, constant turnover, and numerous small shifts are most explanatory.

Conclusions:

  • PPLs offer a powerful and flexible framework for statistical phylogenetic analysis.
  • Automated inference methods like SMC significantly advance the study of evolutionary models.
  • This approach is poised to be broadly applicable to the full spectrum of phylogenetic models.