Particle Gibbs Sampling for Bayesian Phylogenetic inference

TL;DR

This paper introduces an improved combinatorial sequential Monte Carlo method integrated into a particle Gibbs sampler for more efficient Bayesian phylogenetic inference, addressing previous limitations in sampling tree space.

Contribution

A novel, more efficient proposal distribution for CSMC integrated into particle Gibbs, enabling better sampling of phylogenetic trees and parameters, with parallelization capabilities.

Findings

Enhanced sampling efficiency in phylogenetic tree inference

Effective parallelization of the algorithm

Validated improvements through numerical experiments

Abstract

The combinatorial sequential Monte Carlo (CSMC) has been demonstrated to be an efficient complementary method to the standard Markov chain Monte Carlo (MCMC) for Bayesian phylogenetic tree inference using biological sequences. It is appealing to combine the CSMC and MCMC in the framework of the particle Gibbs (PG) sampler to jointly estimate the phylogenetic trees and evolutionary parameters. However, the Markov chain of the particle Gibbs may mix poorly if the underlying SMC suffers from the path degeneracy issue. Some remedies, including the particle Gibbs with ancestor sampling and the interacting particle MCMC, have been proposed to improve the PG. But they either cannot be applied to or remain inefficient for the combinatorial tree space. We introduce a novel CSMC method by proposing a more efficient proposal distribution. It also can be combined into the particle Gibbs sampler framework to infer parameters in the evolutionary model. The new algorithm can be easily parallelized by allocating samples over different computing cores. We validate that the developed CSMC can sample trees more efficiently in various particle Gibbs samplers via numerical experiments. Our implementation is available at https://github.com/liangliangwangsfu/phyloPMCMC