This lecture covers selected advanced topics in computational statistics. This year the focus will be on graphical modelling.
Students learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes.
The main focus will be on graphical models in various forms: Markov properties of undirected graphs; Belief propagation; Hidden Markov Models; Structure estimation and parameter estimation; inference for high-dimensional data; causal graphical models
Prerequisites / Notice
We assume a solid background in mathematics, an introductory lecture in probability and statistics, and at least one more advanced course in statistics.