Neotropical tetras of the family Characidae form the largest and most taxonomically complex
clade within the order Characiformes. Previous phylogenetic relationships concur on the …

Bayesian phylogenetic analyses estimate posterior distributions of phylogenetic tree
topologies and other parameters using Markov chain Monte Carlo (MCMC) methods. Before …

The marginal likelihood of a model is a key quantity for assessing the evidence provided by
the data in support of a model. The marginal likelihood is the normalizing constant for the …

Recent advances in statistical machine learning techniques have led to the creation of
probabilistic programming frameworks. These frameworks enable probabilistic models to be …

The ongoing global pandemic has sharply increased the amount of data available to
researchers in epidemiology and public health. Unfortunately, few existing analysis tools are …

The large state space of gene genealogies is a major hurdle for inference methods based
on Kingman's coalescent. Here, we present a new Bayesian approach for inferring past …

We construct a zig-zag process targeting a posterior distribution defined on a hybrid state
space consisting of both discrete and continuous variables. The construction does not …

Bayesian inference of phylogeny with Markov chain Monte Carlo plays a key role in the
study of evolution. Yet, this method still suffers from a practical challenge identified more …

Time-calibrated phylogenies are key to macroevolutionary hypothesis testing and parameter
inference, but their estimation is difficult when the number of tips is large. Despite its …

Phylogenetics has seen a steady increase in data set size and substitution model
complexity, which require increasing amounts of computational power to compute …