Universal probabilistic programming offers a powerful approach to statistical phylogenetics.

ABSTRACT: Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabilistic graphical models, but this formalism can only partly express phylogenetic problems. Here, we show that universal probabilistic programming languages (PPLs) solve the expressivity problem, while still supporting automated generation of efficient inference algorithms. To prove the latter point, we develop automated generation of sequential Monte Carlo (SMC) algorithms for PPL descriptions of arbitrary biological diversification (birth-death) models. SMC is a new inference strategy for these problems, supporting both parameter inference and

SUBMITTER: Ronquist F 

PROVIDER: S-EPMC7904853 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature