Bayesian Statistics
10 credits
Syllabus, Master's level, 1MS900
A revised version of the syllabus is available.
- Code
- 1MS900
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Mathematics A1N
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 10 March 2016
- Responsible department
- Department of Mathematics
Entry requirements
120 credits including 90 credits mathematics with Regression Analysis and Inference Theory II
Learning outcomes
In order to pass the course the student should be able to
- give an account of the philosophy of Bayesian models and their specific model assumptions;
- choose suitable informative and non-informative prior distributions;
- derive posterior distributions;
- apply computer intensive methods for approximating the posterior distribution using R;
- make correct inference from theoretical and approximated posterior distributions;
- be able to interpret the results obtained by Bayesian methods.
Content
Decision theoretic foundations. The minimaxity. The choice of prior distributions. Conjugate families. Bayesian point estimation. Bayesian tests. MCMC. Gibbs sampler. Bayesian model choice. Empirical Bayes extension.
Instruction
Lectures and computer sessions.
Assessment
Written examination at the end of the course. Compulsory assignments during the course.