Summer 2021: Bayesian Statistics II

Contents

Bayesian thinking differs from frequentist statistics in its interpretation of probability and uncertainty. It complements the existing statistical toolbox with powerful methods for simulation and inference. The lectures Bayesian Statistics I and II aim to familiarize the students to the Bayesian approach. The first part deals with the theoretical fundamentals and the principles of estimating, testing, forecasting and model assessment. In addition, Bayesian regression concepts and computer-intensive simulation methods such as Markov chain Monte Carlo (MCMC) are introduced. The second part complements and deepens these topics, for example by Bayesian nonparametric density estimation, Bayesian model choice and Approximate Bayesian Computing (ABC).

General information

Lecturers: Prof. Dr. Christiane Fuchs (lectures), Houda Yaqine (exercises)

Type: Lecture with optional exercises

Study achievements (Studienleistungen): Study achievements for the exercise class can be fulfilled by preparation of the exercise sheets, one submission of a solution, and active participation in discussions.

Recommended prerequisites: Good knowledge of statistics (esp. (conditional) densities/probabilities, likelihood inference, regression) and R, Bayesian Statistics I

Module allocation: see eKVV (lecture) and eKVV (exercises)

Place: Due to the current situation, the lectures and exercise classes take place in an online format. Registered students receive more information via email.

Dates: The lectures and exercise classes take place on Thursdays as follows:

Literature

• Lee: Bayesian Statistics. Wiley, 4th edition.
• Gelman et al.: Bayesian Data Analysis. CRC Press, 3rd edition.

Material

Lecture slides, exercise sheets and further material are made available via LernraumPlus.

This class is supported by DataCamp, a learning platform for data science. Members of this class can access all courses for free. The invitation link is made available through LernraumPlus.