NSCPHD1077  Bayesian Statistics

Volume 2016/2017
Education

PhD programme in Statistics

Content

 

PLEASE NOTE         

The PhD course database is under construction. If you want to sign up for this course, please click on the link in order to be re-directed. Link: https://phdcourses.ku.dk/nat.aspx

 

  • 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

 

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

Basic understanding of mathematical statistics including conditional distributions
Lectures and theoretical exercises
Credit
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
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.

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