Bayesian Statistics

You are here


 Bayesian StatisticsPE&RC logo klein transparant_0.png

To be announced


This course offers a hands-on, example-driven approach to teaching the core concepts and tools of Bayesian data analysis. It will be a course in which tutorials are followed by illustrative practicals.


Classical statistics offers a powerful toolbox for data analysis. This toolbox, however, may not always be sufficiently flexible for modern data situations. For example, some situations benefit from data integration or the inclusion of information from other sources than your data. The Bayesian framework allows for the integration and inclusion of information from many sources as well as a natural quantification of uncertainty in subsequent analysis. It offers these benefits for standard statistical models as well as highly customized models. This flexibility is the reason why the machinery of Bayesian inference has been successfully used in, for example, code-cracking, self-driving cars, genomic prediction, and climate-change prediction. Bayesian inference now underlies many advances in artificial intelligence, machine learning, and data science.

Learning outcomes

After completion you are able to:

  • Explain the differences between classical and Bayesian analysis of data
  • Recognize questions and situations that ask for a Bayesian approach to data analysis
  • Use state-of-the-art computational approaches to Bayesian data analysis
  • Effectively set up, perform, and communicate a Bayesian data analysis.
General information
Target Group The course is aimed at PhD candidates and other academics
Group Size 24 participants
Course duration 4 days
Language of instruction English
Frequency of recurrence Once a year (Spring)
Number of credits 1.2 ECTS
Recommended literature Lambert, B. (2018). A Student’s Guide to Bayesian Statistics. London: SAGE. 498p.
Prior knowledge Basic Statistics
Software We will use R as an interface to STAN, a state-of-the-art platform for Bayesian modeling based on the powerful Hamiltonian Monte Carlo sampler.
Lecturers Dr. Gerrit Gort (Biometris, Wageningen University), Dr. Carel Peeters (Biometris, Wageningen University)
Location Wageningen Campus
Accommodation Accommodation is not included in the fee of the course, but there are several possibilities in Wageningen. For non-WUR PE&RC members 50% of the accommodation costs can be reimbursed with a maximum of €30,- per night, please contact the PE&RC Office ( for more information.
For information on B&B's and hotels in Wageningen please visit Another option is Short Stay Wageningen. Furthermore Airbnb offers several rooms in the area. Finally, there are a number of groups on Facebook where students announce subrent possibilities and things like that. Examples include: Wageningen Room Subrent, Wageningen Room Sublets, Room Rent Wageningen, and Wageningen Student Plaza. Note that besides the restaurants in Wageningen, there are also options to have dinner on Wageningen Campus.
More information

Claudius van de Vijver (PE&RC)
Phone: +31 (0) 317 485116

Registration of interest

At this moment, this course is not scheduled yet. However, if you register your interest in this activity below, we will inform you as soon as the course is scheduled and registration of participation is opened.