Bayesian Methods
Syllabus, Master's level, 1MS017
This course has been discontinued.
- Code
- 1MS017
- 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, 15 March 2007
- Responsible department
- Department of Mathematics
Entry requirements
BSc, Analysis of Regression and Variance
Learning outcomes
In order to pass the course (grade 3) the student should be able to
Higher grades, 4 or 5, require a higher level of proficiency. The student should be able to treat and solve problems of greater complexity, i.e. problems requiring a combination of ideas and methods for their solution, and be able to give a more detailed account of the proofs of important theorems and by examples and counter-examples be able to motivate the scope of various results.
Requirements concerning the student's ability to present arguments and reasoning are greater.
Content
Bayes formula. Informative and non-informative á priori distributions. Á posteriori distributions. Hierarchical models. Linear models. Bayesian inference. Markov Chain Monte Carlo (MCMC) methods.
Instruction
Lectures, problem solving sessions and computer-assisted laboratory work.
Assessment
Written examination at the end of the course. Compulsory assignments and laboratory work during the course.
Reading list
No reading list found.