This course offers an advanced understanding of probabilistic inference of Phylogenetic Comparative Methods (PCM), exploiting the capabilities of the Julia language. Participants will gain a deeper knowledge of the stochastic processes, their inference and computation behind PCMs as well as their biological interpretations.
We will start with an introduction to Julia language, a powerful new language for numerical computing that combines high performance with high-level syntax, attaining comparable speeds as C, yet remaining accessible to programming initiates. We will then overview probabilistic inference within a Bayesian framework, reviewing basic probability concepts and posterior parameter estimation. Finally, most of the course will then delve into the main three PCM: trait and biogeographic evolution, and a deeper emphasis on diversification models. Topics covered include basic foundations (i.e., diffusion processes such as Brownian motion, time-continuous Discrete Markov models, birth-death models) to then build-up to the more
advanced models that allow for interdependence between processes (i.e., environmental and geographic diversification, inference of biotic interactions). The course will combine introductory lectures and hands-on exercises.