Improved Variational Bayesian Phylogenetic Inference using Mixtures
Oskar Kviman Ricky Molén Jens Lagergren
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.