Fidelity of Hyperbolic Space for Bayesian Phylogenetic Inference

TL;DR

This paper explores embedding genomic sequences into hyperbolic space to improve Bayesian phylogenetic inference, demonstrating high fidelity in recovering phylogenetic trees and analyzing the effects of embedding parameters.

Contribution

It introduces a novel hyperbolic space embedding approach combined with MCMC for Bayesian phylogenetics, enhancing inference accuracy and efficiency.

Findings

High fidelity in recovering splits and branch lengths

Embedding parameters significantly affect MCMC performance

Potential for gradient-based navigation in tree space

Abstract

Bayesian inference for phylogenetics is a gold standard for computing distributions of phylogenies. It faces the challenging problem of. moving throughout the high-dimensional space of trees. However, hyperbolic space offers a low dimensional representation of tree-like data. In this paper, we embed genomic sequences into hyperbolic space and perform hyperbolic Markov Chain Monte Carlo for Bayesian inference. The posterior probability is computed by decoding a neighbour joining tree from proposed embedding locations. We empirically demonstrate the fidelity of this method on eight data sets. The sampled posterior distribution recovers the splits and branch lengths to a high degree. We investigated the effects of curvature and embedding dimension on the Markov Chain's performance. Finally, we discuss the prospects for adapting this method to navigate tree space with gradients.