After successful completion, students are familiar with traditional and modern concepts of statistical inference and prediction within the Bayesian paradigm. They can independently build statistical models for various problem settings, and interpret their findings coherently.

Lecture, exercises, case studies.

Preliminary outline (to be adjusted to students' prior knowledge):From Bayes’ Rule to Bayes’ TheoremA First Bayesian Analysis of Count Data and ProportionsThe Bayesian Approach to Regression Modeling of Normal and Non-Normal DataBayesian Predictive Analysis and Model DiagnosticsBayesian Model SelectionComputational Tools for Bayesian InferenceSelected MCMC Methods for Computational Bayesian InferenceComputational Tools for Model Comparison and Model Specification UncertaintyIf time allows and depending on students' interests: Bayesian Time Series AnalysisIf time allows and depending on students' interests: State Space Modeling and Time-Varying Parameter ModelsIf time allows and depending on students' interests: Hierarchical Bayesian ModelsIf time allows and depending on students' interests: Bayesian Factor Analysis

Elementary probability calculus

To maximize the learning outcome, please combine with "Bayesian Statistics" (lecture).

TBA

Weekly exercises (to be solved and presented by the students) and case studies (to be solved and handed in).

Contents of lecture, exercises, and case studies.

Weekly exercises (to be solved and presented by the students) and case studies (to be solved and handed in).