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Bayesian Methods 1

Vak
2022-2023

Admission requirements

Basic probability and statistics, a good notion of regression models and familiarity with R

Description

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.

Course Objectives

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.

Timetable

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.

Assessment method

Written examination at end of the course.

Reading list

To be decided

Registration

From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.

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Contact

b [dot] mertens [at] lumc [dot] nl

Remarks

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