STAT207: Intermediate Bayesian Statistical Modeling

Hierarchical modeling, linear models (regression and analysis of variance) from the Bayesian point of view, intermediate Markov chain Monte Carlo methods, generalized linear models, multivariate models, mixture models, hidden Markov models. Prerequisite(s): STAT 206B. Enrollment is restricted to graduate students or by permission of instructor.

5 credits

Year	Fall	Winter	Spring	Summer
2024-25			Section 01 Richard Li (zli439)
2023-24			Section 01 Richard Li (zli439)
2022-23			Section 01 Richard Li (zli439)
2021-22			Section 01 Richard Li (zli439)
2020-21			Section 01 Bruno Sansó (bsanso)

Comments

Formerly AMS 0207

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