NMAK16002U Bayesian Statistics
Volume 2016/2017
Education
MSc Programme in Statistics
Content
- The Bayesian paradigm
- Sufficiency and likelihood
- Prior and posterior distributions
- Decision theoretic foundations
- Conjugate prior distributions
- Default prior distributions
- Bayesian parameter estimation
- Bayesian computation
- Bayes factors and model choice
- Bayesian asymptotics
- Empirical Bayes methods
Learning Outcome
Knowledge:
Basic knowledge of the topics covered
Skills:
- Discuss and understand basics of the Bayesian paradigm
- Understand how decision theory underpins Bayesian inference
- Understand methods for constructing prior distributions
- Discuss and understand basic principles for Bayesian model choice
Competences:
- Ability to use standard software for simple modelling and Bayesian computation
- Ability to construct and perform a Bayesian analysis of statistical models
Recommended Academic Qualifications
Basic understanding of
mathematical statistics including conditional distributions. Stat1
+ Stat2 or equivalent is sufficient.
Teaching and learning methods
4 hours of lectures and 3
hours of exercises per week for seven weeks.
Workload
- Category
- Hours
- Exam
- 27
- Lectures
- 28
- Practical exercises
- 3
- Preparation
- 130
- Theory exercises
- 18
- Total
- 206
Sign up
Self Service at KUnet
As
an exchange, guest and credit student - click here!
Continuing Education - click here!
Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Written assignment, 27 hours---
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner
- Re-exam
As for the ordinary exam
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course information
- Language
- English
- Course code
- NMAK16002U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 3
- Schedule
- A
- Course capacity
- No restrictions/ no limitations
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Course responsibles
- Steffen L. Lauritzen (9-756a7e7b727d836e7749766a7d7137747e376d74)
Lecturers
Steffen L. Lauritzen
Saved on the
09-03-2016