Module 9: PyRate and AI methods to reconstruct biodiversity

Estimation of diversity and diversification using Bayesian and deep learning models

In this part of the course we will go over several models in infer preservation, speciation, and extinction processes from fossil occurrence data. Most of the methods do not require a joint estimation of the underlying phylogenetic tree connecting the lineages (which can be both a good and a bad thing).

The classes will include general explanations of how Bayesian inference and MCMC machinery work, and how supervised and unsupervised deep neural networks can be used in the context of macroevolution.

We will refer to the materials, data, and tutorials available in this repository, developed by Torsten Hauffe, Rebecca Cooper, Daniele Silvestro (and several other collaborators we are very grateful to).

PyRate slides
BDNN model slides
NN slides
DeepDive slides
BBB model slides