Introduction to Bayesian Statistics and Information Theory (12 C, 12 SWS) [M.Bio.323]

Learning objectives / Core skills The students learn the basic concepts and main applications of Bayesian Statistics, in particular Bayesian probabilities, parameter estimation and Bayesian credible intervals, importance and choice of prior distributions based on prior knowledge, Bayesian hypothesis testing, model tests and MCMC methods. All concepts will be presented in lectures and worked with in hands-on computer assignments. The module closes with a foray into information theory.

Examination requirements: Knowledge of the foundations of Bayesian probabilities and statistics and the ability to solve simple classic problems in Bayesian Inference.

Courses and examinations
1. Lecture: »Introduction to Bayesian Inference and Information Theory « (3 SWS)
2. Seminar: »Classical problems in Bayesian Interference« (1 SWS)
3. Practical course: »Programming course« (8 SWS)

Examination: written examination (90 minutes)

Selection options

Number of repeat examinations permitted

Course frequency: Academic Term
Each winter-semester

One semester


Maximum number of students
Students of the study programs: MSc Molecular Life Sciences - Microbiology, Biotechnology and Biochemistry / MSc Applied Computer Science or MSc Applied Data Science have to contact before Oct 10th to find out about free capacities.

Person responsible for module
Prof. Dr. Michael Wibral