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 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 Statement
The authors have declared no competing interest.
