Methods and Applications for Bayesian Phylodynamic Inference

Bayesian phylodynamic analyses of epidemics comprise an ever-increasing amount of genomic sequence data and associated metadata. This increased dimensionality requires the development of novel inference methodologies that allow to incorporate large amounts of data into such analyses. The aim of this PhD is hence to develop approaches that enable augmenting ongoing analyses with novel data as well as to develop novel transition kernels that can explore the search space in a more efficient manner than current techniques.