STAT206: Applied Bayesian Statistics

Introduces Bayesian statistical modeling from a practitioner's perspective. Covers basic concepts (e.g., prior-posterior updating, Bayes factors, conjugacy, hierarchical modeling, shrinkage, etc.), computational tools (Markov chain Monte Carlo, Laplace approximations), and Bayesian inference for some specific models widely used in the literature (linear and generalized linear mixed models). (Formerly Classical and Bayesian Inference.)

5 credits

Year	Fall	Winter	Spring	Summer
2023-24		Section 01 David Draper (draper)
2022-23		Section 01 David Draper (draper)
2021-22		Section 01 David Draper (draper)
2020-21		Section 01 David Draper (draper)

Comments

Formerly AMS 0206

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