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.

1. The file 'SupMatAdaptiveTreeProposals.pdf' contains the supplemental methods, figures and tables referenced in the main text.
2. The data and software used for this study can be found at the following URL: https://bitbucket.org/XavMeyer/coevrj/src/master/. The README.md file in this GIT repository provides you all the information required to replicate the simulation conducted in this study.