Generative Flow Networks (GFlowNets) offer a novel approach to phylogenetic inference, providing competitive results in Bayesian and parsimony-based analyses.
Generative Flow Networks (GFlowNets) offer a novel approach to phylogenetic inference, producing diverse and high-quality evolutionary hypotheses.
This paper presents a novel approach for inferring diversification rates in phylogenetic trees using Approximate Bayesian Computation (ABC) with Markovian Binary Trees (MBTs), offering a more flexible and potentially accurate alternative to traditional likelihood-based methods.