Bayesian Statistics

Course content

 

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  • 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
Education

PhD programme in Statistics

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

 

Lectures and theoretical exercises

C. P. Robert. The Bayesian Choice. 2nd edition. Springer-Verlag 2001. Paperback edition 2007.

Basic understanding of mathematical statistics including conditional distributions

ECTS
7,5 ECTS
Type of assessment
Written assignment, 27 hours
Written assignment
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Theory exercises
  • 18
  • Practical exercises
  • 3
  • Exam
  • 27
  • Preparation
  • 130
  • English
  • 206