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Doctoral course

Bayesian Statistics, 7.5 credits

Course information

Research education subject

  • Economics
  • Statistics

Course Syllabus

Course Syllabus

Course period

September 2024 - November 2024

Contacts

Course content

  • Bayesian inference theory
  • Simulation of posterior distributions
  • Regression
  • Hierarchical models
  • Data augmentation and latent variables
  • Diagnostics and model choice

Intended course learning outcomes

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

  1. Understanding of the basic concepts in Bayesian Statistics,
  2. Ability to independently formulate a suitable statistical model including the choice of prior distribution,
  3. Ability to analyse (with concrete examples) how the posterior distribution can be evaluated with simulation methods,
  4. Ability to communicate relevant aspects of the modeling problem and the results of the statistical analysis,
  5. Ability to critically examine, evaluate and compare Bayesian statistical models.