AMS206: Classical and Bayesian Inference

*****COURSES ARE SUBJECT TO CHANGE*****

Introduction to Bayesian statistical modeling from a practitioner¿s perspective. The course 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).

5 Credits

YearFallWinterSpringSummer
2017-18
2016-17
2015-16
2014-15
2013-14
2012-13
2011-12
2010-11
  • Section 01
    David Draper (draper)
    Telecast to SVC
2009-10
2008-09
2007-08
2006-07
2005-06
2004-05
2003-04
2002-03

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