Supplementary Materials - Adaptive Tree Proposals for Bayesian Phylogenetic Inference

Bayesian inference of phylogeny with MCMC plays a key role in the study of
evolution. Yet, this method still suffers from a practical challenge
identified more than two decades ago: designing tree topology proposals
that efficiently sample tree spaces. In this study, I introduce the
concept of adaptive tree proposals for unrooted topologies, that is tree
proposals adapting to the posterior distribution as it is estimated. I use
this concept to elaborate two adaptive variants of existing proposals and
an adaptive proposal based on a novel design philosophy in which the
structure of the proposal is informed by the posterior distribution of
trees. This dataset contains a document (PDF) detailing the key concepts
presented in this study: How to approximate the posterior distribution of
trees How to build "ergodic" adaptive tree proposals An example
of how to build path-based adaptive tree proposals Additionally, this
document contains extended results (with absolute value), and an
additional result showing the relation between the amount phylogenetic
information contained in an alignment and the behavior of adaptive tree
proposals.