Graphical models with Bayes spaces

Graphical models are probabilistic models for modelling relationships between random variables using graphs, with many applications in statistics - especially Bayesian statistics - and machine learning. The aim of this thesis will be to reformulate these models by using so-called Bayes spaces to represent the probability density functions and to explore the associated theoretical and application potential.