Statistics, Bayesian Statistics, Second Cycle, 5 credits

Learning outcomes

After completed studies, the student shall be able to

• understand the basic concepts in Bayesian Statistics,
• independently formulate a suitable statistical model including the choice of prior distribution,
• analyse (with concrete examples) how the posterior distribution can be evaluated with simulation methods,
• communicate relevant aspects of the modeling problem and the results of the statistical analysis,
• critically examine, evaluate and compare Bayesian statistical models.

Content

• Bayesian inference theory
• Simulation of posterior distributions
• Regression
• Hierarchical models
• Data augmentation and latent variables
• Diagnostics and model choice

Examinations and grades

Examination, 5 credits (Code: A001)
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may determine which grading system is to be used. The grade must be determined by a teacher specifically nominated by the university (the examiner).

In accordance with university regulations on grading systems for first and second-cycle courses and study programmes (Vice-Chancellor’s decision ORU 2018/00929), one of the following grades is to be used: fail (U), pass (G) or pass with distinction (VG). For courses included in an international master’s programme (60 or 120 credits) or offered to the university’s incoming exchange students, the A to F grading scale is to be used. The vice-chancellor, or a person appointed by them, may decide on exceptions from this provision for a specific course, if there are special grounds for doing so.

The grades used on this course are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Modes of assessment

Examination, 5 credits (Code: A001)
Written Examination

For students with a documented disability, the university may approve applications for adapted or other modes of assessment.

For further information, see the university's local examination regulations.

Specific entry requirements

First-cycle courses of 90 credits in Statistics, alternatively first-cycle courses of 30 credits in statistics and 60 credits in mathematics, alternatively first-cycle courses of 60 credits in statistics including 7.5 credits of Statistical theory and 7.5 credits of Regression analysis/Econometrics. The applicant must also have the courses Probability Theory, Second Cycle, 5 credits and Computational Statistics, Second Cycle, 7.5 credits, as well as qualifications corresponding to the course "English 6" or "English B" from the Swedish Upper Secondary School.

For further information, see the university's admission regulations.

Other provisions

The course is given in English.

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.

Reading list and other learning resources

Required Reading

Gelman m.fl. (latest edition)
Bayesian Data Analysis
Chapman & Hall