Bayesian Data Analysis



B = Basic
M = Intermediate
A = Advanced

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Further and more detailed information, including the schedule, can be found in the current course tables in the syllabus of the respective course, if the course is offered in the next sessions. The following text serves as information on what can be expected in terms of content in the course.

Learning objectives: To understand what the Bayesian approach to statistical modeling is and to appreciate the differences between the Bayesian and Frequentist approaches. The students will be able to estimate a wide variety of models in the Bayesian framework and to adjust example code to fit their specific modeling needs. Course content: -Theory/foundations of the Bayesian approach including: -objective vs subjective probability -how to derive and incorporate prior information -the basics of MCMC sampling -assessing convergence of Markov Chains -Bayesian difference of means/ANOVA -Bayesian versions of: Linear models, logit/probit (dichotomous/ordered/unordered choice models), Count models, Latent variable and measurement models, Multilevel models -presentation of results