Researchers studying the evolution of viral pathogens and other organisms increasingly
encounter and use large and complex data sets from multiple different sources. Statistical …

Phylogenetic inference, grounded in molecular evolution models, is essential for
understanding the evolutionary relationships in biological data. Accounting for the …

Phylogenetics is a branch of computational biology that studies the evolutionary
relationships among biological entities. Its long history and numerous applications …

We cast the resampling step in particle filters (PFs) as a variational inference problem,
resulting in a new class of resampling schemes: variational resampling. Variational …

This paper presents a new approach to automatically discovering accurate models of
complex time series data. Working within a Bayesian nonparametric prior over a symbolic …

Phylogenetics is a classical methodology in computational biology that today has become
highly relevant for medical investigation of single-cell data, eg, in the context of development …

Gradients of probabilistic model likelihoods with respect to their parameters are essential for
modern computational statistics and machine learning. These calculations are readily …

Designing flexible probabilistic models over tree topologies is important for developing
efficient phylogenetic inference methods. To do that, previous works often leverage the …

We present VBPI-Mixtures, an algorithm designed to enhance the accuracy of phylogenetic
posterior distributions, particularly for tree-topology and branch-length approximations …

Mixture variational distributions in black box variational inference (BBVI) have demonstrated
impressive results in challenging density estimation tasks. However, currently scaling the …