Bayesian Statistics, 7.5 credits
Bayesiansk statistik, 7.5 hp
732G43
Main field of study
StatisticsCourse level
First cycleCourse type
Programme courseExaminer
Bertil WegmannCourse coordinator
Bertil WegmannDirector of studies or equivalent
Ann-Charlotte HallbergCourse offered for | Semester | Weeks | Language | Campus | ECV | |
---|---|---|---|---|---|---|
F7KSA | Bachelor´s Programme in Statistics and Data Analysis | 5 (Autumn 2019) | 201944-202003 | Swedish | Linköping, Valla | E |
Main field of study
StatisticsCourse level
First cycleAdvancement level
G2FCourse offered for
- Bachelor´s Programme in Statistics and Data Analysis
Intended learning outcomes
After completion of the course, the student should be able to
- describe the main concepts in Bayesian statistics
- explaine the differences between frequentist and Bayesian statistics
- use the most common statistical methods in Bayesian inference
- choose suitable models for Bayesian inference of various practical problems
- use statistical software to solve statistical problems
- compare the results from frequentist and Bayesian methods on given practical problem
Course content
The course consists of general concepts and methods in Bayesian statistics. In addition, MCMC is implemented as a tool to estimate more complicated models in which an analytical form of the posterior is not possitble.
Contents:
- subjective probabilities
- Bayes' theorem
- prior distribution
- sensitivity analysis of prior distributions
- likelihood function
- posterior distribution
- credible interval
- model evaluation
- MCM
Teaching and working methods
The teaching comprises lectures, tutorials, seminars, and computer sessions. Homework and independent study are a necessary complement to the course.
Examination
Written examination. Written reports. Detailed information about the examination can be found in the courses study guide.
Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.
Students who have passed an examination may not retake it in order to improve their grades.
Grades
Three-grade scale, U, G, VGOther information
Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus.
The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.
Department
Institutionen för datavetenskapCode | Name | Scope | Grading scale |
---|---|---|---|
INL1 | Examination | 4 credits | U, G |
TEN1 | Examination | 3.5 credits | U, G |
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