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Summer school on Advanced Bayesian Methods

Summer school on Advanced Bayesian Methods

The Interuniversity Institute for Biostatistics and statistical Bioinformatics organizes for the third time a summer school on Bayesian methods. During one week, two courses will be taught on specific topics in Bayesian methodology. The focus is on Bayesian methods that are relevant for the applied statistician. Special attention will be devoted to novel statistical methodology.


In the third edition of the summer school, the following two courses will be organized in Leuven from 23 to 27 September 2019:
• Two-day course (23-24 September) on Advanced Simulation and Numerical Methods for Bayesian Statistics by Dr. Mark Girolami (Imperial College, London and The Alan Turing Institute, UK)
• Three-day course (25-27 September) on Bayesian Variable Selection Methods by Dr. Ioannis Nzouftras (Athens University of Economics and Business, Greece)


The target audience of summer school are statisticians and/or epidemiologists with a sound background in statistics, but also with background in Bayesian methodology. In both courses, practical sessions are organized, so participants are asked to bring along their laptop with the appropriate software (to be announced) pre-installed.

The registration costs for the courses are:

Two-day course 

I-Biostat member € 50 
PhD student € 200 
Quetelet member € 200 
Academic € 300 
ISBA member € 300
Research institute € 300 
Industry € 900

 

Three-day course

I-Biostat member: € 50
PhD student: € 250
Quetelet member € 250
Academic: € 400
ISBA member € 400
Research institute: € 400
Industry € 1200


Note that one is registered to the course, ONLY when the registration costs have been paid. The deadline for registration is August 31, 2019.

More information about the courses and practicalities (registration, location, transportation, etc.) can be found in due time on https://ibiostat.be.

Please reserve already this week in September 2019!

For additional questions, please contact Kirsten Verhaegen ().