Improved Variational Bayesian Phylogenetic Inference using Mixtures
Abstract: We introduce VBPI-Mixtures, an innovative algorithm aimed at improving the precision of phylogenetic posterior distributions, with a focus on accurately approximating tree-topologies and branch lengths. Although Variational Bayesian Phylogenetic Inference (VBPI) a state-of-the-art black-box variational inference (BBVI) framework has achieved significant success in approximating these distributions, it faces challenges in dealing with the multimodal nature of tree-topology posteriors. While advanced deep learning techniques like normalizing flows and graph neural networks have enhanced VBPI's approximations of branch-length posteriors, there has been a gap in improving its tree-topology posterior approximations. Our novel VBPI-Mixtures algorithm addresses this gap by leveraging recent advancements in mixture learning within the BBVI domain. Consequently, VBPI-Mixtures can capture distributions over tree-topologies that other VBPI algorithms cannot model. We demonstrate superior performance on challenging density estimation tasks across various real phylogenetic datasets.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: N/A
Assigned Action Editor: ~Francisco_J._R._Ruiz1
Submission Number: 2840
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