Bayesian Phylogenetic Inference

Bayesian phylogenetic inference aims to reconstruct evolutionary relationships between biological entities by probabilistically inferring tree-like structures (phylogenies) from molecular data. Recent research focuses on developing more efficient inference methods, moving beyond traditional Markov Chain Monte Carlo approaches by employing variational inference techniques and generative flow networks (GFlowNets) to navigate the complex space of possible phylogenies. These advancements improve the speed and accuracy of phylogenetic inference, particularly for large datasets, and are impacting fields like epidemiology and cancer research through improved analysis of evolutionary processes. The development of novel sampling schemes and flexible prior models further enhances the accuracy and robustness of these methods.