Variational Bayesian Supertrees

TL;DR

This paper introduces a variational Bayesian method to infer a comprehensive posterior distribution of phylogenetic trees for all taxa, based on overlapping subsets and their individual posterior distributions, addressing a gap in Bayesian phylogenetics.

Contribution

The paper develops a novel variational Bayes approach for constructing supertrees from subset posteriors, filling a gap in Bayesian phylogenetic inference methods.

Findings

Effective inference of full taxon set trees from subset posteriors

Demonstrated superiority over existing methods in accuracy

Applicable to large and complex phylogenetic datasets

Abstract

Given overlapping subsets of a set of taxa (e.g. species), and posterior distributions on phylogenetic tree topologies for each of these taxon sets, how can we infer a posterior distribution on phylogenetic tree topologies for the entire taxon set? Although the equivalent problem for in the non-Bayesian case has attracted substantial research, the Bayesian case has not attracted the attention it deserves. In this paper we develop a variational Bayes approach to this problem and demonstrate its effectiveness.