PT  - JOURNAL ARTICLE
AU  - Fredrik Ronquist
AU  - Jan Kudlicka
AU  - Viktor Senderov
AU  - Johannes Borgström
AU  - Nicolas Lartillot
AU  - Daniel Lundén
AU  - Lawrence Murray
AU  - Thomas B. Schön
AU  - David Broman
TI  - Probabilistic programming: a powerful new approach to statistical phylogenetics
AID  - 10.1101/2020.06.16.154443
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.06.16.154443
4099  - http://biorxiv.org/content/early/2020/06/18/2020.06.16.154443.short
4100  - http://biorxiv.org/content/early/2020/06/18/2020.06.16.154443.full
AB  - 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 model expression problem, while still supporting automated generation of efficient inference algorithms. To illustrate the power of the approach, we use it to generate sequential Monte Carlo (SMC) algorithms for recent biological diversification models that have been difficult to tackle using traditional approaches. This is the first time that SMC algorithms have been available for these models, and the first time it has been possible to compare them using model testing. Leveraging these advances, we re-examine previous claims about the performance of the models. Our work opens up several related problem domains to PPL approaches, and shows that few hurdles remain before PPLs can be effectively applied to the full range of phylogenetic models.Competing Interest StatementThe authors have declared no competing interest.