Module MA4948-KP05

Duration

1 Semester

Turnus of offer

irregularly

Credit points

5

Course of studies, specific fields and terms:

• Master CLS 2023, optional subject, mathematics
• Bachelor CLS 2023, optional subject, mathematics
• Master CLS 2016, optional subject, mathematics
• Bachelor CLS 2016, optional subject, mathematics

Classes and lectures:

• Introduction to Bayesian statistics (exercise, 1 SWS)
• Introduction to Bayesian statistics (lecture, 2 SWS)

Workload:

• 45 hours exam preparation
• 60 hours private studies
• 45 hours in-classroom work

Contents of teaching:

• Bayesian perspective of uncertainty
• Versions of the Theorem of Bayes
• Conjugacy and conditional independence
• Elicitation of prior information
• Linear and generalized linear regression models in Bayesian framework
• Gibbs sampler, Metropolis-Hastings and other MCMC algorithms
• Models of missing values
• Prior and model robustness
• Connections to the non-Bayesian approach and asymptotics
• Empirical Bayes
• Applications to laboratory experiments, meta-analysis, machine learning, and decisions

Qualification-goals/Competencies:

• Students know the framework of Bayesian data analysis
• They understand the interplay of prior and accumulating information
• They are able to apply Bayesian linear and generalized linear models for data analysis
• They understand and can perform convergence diagnostics
• They elicit prior information from literature and communicate posterior and predictive results thoughtfully
• They are able to design and code algorithms for customized analyses
• They are able to augment their repertoire of models to fit new applications
• Acquisition of english technical language

Grading through:

• Oral examination

Requires:

• Biostatistics 1 (MA1600-KP04, MA1600, MA1600-MML)
• Biostatistics 2 (MA2600-KP07)

Responsible for this module:

• Prof. Dr. rer. biol. hum. Inke König

Teacher:

• Institute of Medical Biometry and Statistics
• PD Dr. rer. pol. Reinhard Vonthein

Literature:

• Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin : Bayesian Data Analysis ISBN: 0 – 412-03991-5
• Leonhard Held : Methoden der statistischen Inferenz: Likelihood und Bayes ISBN 978-3-8274-1939-2
• Jean-Michel Marin, Christian P. Robert : Bayesian Core: A Practical Approach to Computational Bayesian Statistics ISBN 978-0-387-38983-7

Language:

Notes:

Last Updated:

15.09.2025