Variational Bayesian Supertrees
This addresses a gap in Bayesian phylogenetic analysis for researchers in computational biology, though it appears incremental as it adapts variational methods to an existing problem.
The paper tackles the problem of inferring posterior distributions on phylogenetic tree topologies for a full set of taxa from overlapping subsets, developing a variational Bayes approach and demonstrating its effectiveness.
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.