Course teacher(s)
Maarten JANSEN (Coordinator)ECTS credits
5
Language(s) of instruction
english
Course content
- The Bayesian approach, introduction: information and uncertainty, the central role of Bayes'rule in sequential learning
- Bayes estimators, loss and risk, Bayesian decision rules, admissible decisions
- Choosing the prior: Fisher information, Kullback-Leibler divergence, Shannon entropy, uninformative priors (uniform, Jeffrey's, Maxent, reference), improper priors, conjugate priors
- applications
Objectives (and/or specific learning outcomes)
The goal is to acquire familiarity with Bayesian reasoning, appreciate benefits and drawbacks, compared to frequentist statistics
Prerequisites and Corequisites
Required and Corequired knowledge and skills
Basic concepts of probability theory and statistics
Teaching methods and learning activities
Face to face teaching with illustrations and exercises
References, bibliography, and recommended reading
See material on Université Virtuelle
Other information
Campus
Plaine
Evaluation
Method(s) of evaluation
- written examination
- Other
written examination
- Open book examination
- Open question with short answer
Other
Language(s) of evaluation
- english