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Bayesian Statistics for Data Analysis (IMEN 891E)
Overview
Course Objectives
This course will introduce elementary Bayesian inference and computational methods for data analysis.
Textbook
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
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