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Doctoral course

Bayesian Statistics, 7.5 credits

Course information

Research education subject

  • Economics
  • Statistics

Course Syllabus

Course Syllabus

Course period

January 2022 - March 2022


Course content

  • Bayesian inference theory
  • Simulation methods
  • Regression
  • Models with latent variables
  • Model checking
  • Model choice.

Intended course learning outcomes

To obtain a passing grade, the doctoral student shall demonstrate:

Knowledge and Understanding

  • After completed studies, the student shall have
  • Understanding of basic concepts in Bayesian Statistics
  • Knowledge of the principles underlying the design of a Bayesian Statistical model
  • Knowledge of modern simulation based computational methods for Bayesian statistical analysis.
  • Competence and Skills

After completed studies, the student shall be able to

  • Independently formulate a suitable statistical model including the choice of prior distribution
  • Communicate relevant aspects of the modelling problem and the results of the statistical analysis.
  • Judgement and Approach

After completed studies, the student shall be able to

  • Critically examine, evaluate and compare Bayesian statistical models.