Improved Variational Bayesian Phylogenetic Inference using Mixtures

Oskar Kviman, Ricky Molén, Jens Lagergren

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Keywords: Phylogenetic Inference, Variational Inference, Bayesian Inference, Mixtures, Black-Box Variational Inference

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TL;DR: We use mixtures of subsplit Bayesian networks and branch-length models to improve the previous state-of-the-art posterior estimation results on a series of popular phylogenetic datasets

Abstract: We present VBPI-Mixtures, an algorithm designed to enhance the accuracy of phylogenetic posterior distributions, particularly for tree-topology and branch-length approximations. Despite the Variational Bayesian Phylogenetic Inference (VBPI), a leading-edge black-box variational inference (BBVI) framework, achieving remarkable approximations of these distributions, the multimodality of the tree-topology posterior presents a formidable challenge to sampling-based learning techniques such as BBVI. Advanced deep learning methodologies such as normalizing flows and graph neural networks have been explored to refine the branch-length posterior approximation, yet efforts to ameliorate the posterior approximation over tree topologies have been lacking. Our novel VBPI-Mixtures algorithm bridges this gap by harnessing the latest breakthroughs in mixture learning within the BBVI domain. As a result, VBPI-Mixtures is capable of capturing distributions over tree-topologies that VBPI fails to model. We deliver state-of-the-art performance on difficult density estimation tasks across numerous real phylogenetic datasets.

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Submission Number: 5164