RT Journal Article
SR Electronic
T1 Probabilistic programming: a powerful new approach to statistical phylogenetics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.06.16.154443
DO 10.1101/2020.06.16.154443
A1 Fredrik Ronquist
A1 Jan Kudlicka
A1 Viktor Senderov
A1 Johannes Borgström
A1 Nicolas Lartillot
A1 Daniel Lundén
A1 Lawrence Murray
A1 Thomas B. Schön
A1 David Broman
YR 2020
UL http://biorxiv.org/content/early/2020/07/05/2020.06.16.154443.abstract
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.