Evaluating probabilistic programming and fast variational Bayesian inference in phylogenetics.

ABSTRACT: Recent advances in statistical machine learning techniques have led to the creation of probabilistic programming frameworks. These frameworks enable probabilistic models to be rapidly prototyped and fit to data using scalable approximation methods such as variational inference. In this work, we explore the use of the Stan language for probabilistic programming in application to phylogenetic models. We show that many commonly used phylogenetic models including the general time reversible substitution model, rate heterogeneity among sites, and a range of coalescent models can be implemented using a probabilistic programming language. The posterior probability distributions obtained via the black box variational inference engine in Stan were compared to those obtained with reference implement

SUBMITTER: Fourment M 

PROVIDER: S-EPMC6966998 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature