Basic probability and statistics, a good notion of regression models and familiarity with R
The course provides a basic introduction to Bayesian forms of inference. The concept of Bayesian belief updating is explained, with reference to Bayes theorem. The notions of prior and posterior density are discussed, as well as concepts and issues relating to prior belief specification. Basic forms and examples of Bayesian belief updating in both univariate and multivariate parameter problems are explained in detail, in particular with respect to the exponential family and basic linear regression models. Examples are discussed as well as applications of Bayesian concepts in approximate (non-Bayesian) forms of inference. We also introduce basic notions of Bayesian computation and apply these methods in examples and exercises. We introduce and discuss some key applications of Bayesian approaches to inference from the statistical literature.
You understand the basic concepts of Bayesian inference and are able to recognize these in documented statistical analyses materials. You are able to apply basic methods of Bayesian inference in simple (data-based) examples. You are able to interpret output and formulated results (conclusions) from applications of Bayesian analyses. You are able to critically evaluate basic applications of Bayesian analysis in simple examples and problem settings.
The schedule for the course can be found on MyTimeTable
Mode of instruction
The course makes use of lectures, exercises, homework, discussions and computer sessions.
Written examination at end of the course.
To be decided
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b [dot] mertens [at] lumc [dot] nl