Bayesian Statistics for Data Analysis (IMEN 891E)


Course Objectives

This course will introduce elementary Bayesian inference and computational methods for data analysis.


  • Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A. and Rubin, D. (2013). Bayesian Data Analysis. (3rd edition). CRC Press.

  • Berger, J. (1985). Statistical Decision Theory and Bayesian Analysis. (2nd edition). Springer.

Lecture Slides

  • Chapter 1: Introduction [Slides]

  • Chapter 2: Single Parameter Models [Slides]

  • Chapter 3: Multi Parameter Models [Slides]

  • Chapter 4: Random Number Generation [Slides]

  • Chapter 5: Markov Chain Monte Carlo Methods [Slides]

  • Chapter 6: Hamiltonian Monte Carlo Methods [Slides]

  • Chapter 7: Hierarchical Models [Slides]

  • Chapter 8: Linear Regression Models [Slides]

  • Chapter 9: Model Assessment [Slides]

  • Chapter 10: Finite Mixture Models [Slides]

  • Chapter 11: Latent Dirichlet Allocation [Slides]

  • Chapter 12: Gaussian Process Regression [Slides]

  • Chapter 13: Dirichlet Process Prior [Slides]

  • Chapter 14: Frequentist Properties of Bayes Procedures [Slides]

Lecture Videos