Otto-von-Guericke-Universität Magdeburg


Applied Bayesian Inference SoSe 2017

A revolution in applied statistics

with applications in biometrics, econometrics, ecology and physics

The lecture gives an introduction to Bayesian inference starting from first principles. The Bayesian approach is based on a different paradigm than the classical frequentist approach to statistical inference. Over the last decade, the Bayesian approach has revolutionised many areas of applied statistics such as biometrics, econometrics, market research, statistical ecology and physics. Although the Bayesian approach dates back to the 18th century, its rise and enormous popularity today is due to the advances made in Bayesian computation through computer-intensive simulation methods. Knowledge of Bayesian procedures and software packages have become indispensable in most areas where statistics is applied. Students will be using the software package R for Bayesian computation and will be introduced to WinBUGS and JAGS. Topics covered include: the Bayesian approach, conjugate distributions, prior specification, posterior computation, simulation methods including Markov chain Monte Carlo using the software WinBUGS and JAGS, model checking, and applications to data analysis. The lecture will be given by Prof. Dr. Renate Meyer, University of Auckland, Department of Statistics, an international expert in Bayesian methods and their applications.


Block lecture (2 SWS)
The lecture is meant for Master and interested PhD students in mathematics and statistics as well as for young researchers (such as PhD students or Post-Docs) from any other discipline who already use or intend to use Bayesian statistics in their data analysis.


When:    10.07.2017      -    14.07.2017
                  9:00            -        10:30
                 11:00           -        12:30
                 14:00           -        15:30
Where:     G02-111
               G02-112  (Computerlabor)


In case of questions:





Letzte Änderung: 06.04.2017 - Ansprechpartner: Kerstin Altenkirch