Bayesian Statistics, 7.5 credits

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.