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.
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.
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.
| Target Group | The course is aimed at PhD candidates and other academics |
| Group Size | Max. 24 participants |
| Course duration | 4 days |
| 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. |
| Recommended literature | Lambert, B. (2018). A Student’s Guide to Bayesian Statistics. London: SAGE. 498p. |
| Lecturers | Dr. Gerrit Gort (Biometris, Wageningen University), Dr. Carel Peeters (Biometris, Wageningen University), Dr. Shota Gugushvili (Biometris, Wageningen University) |
| FEE1 | |
| PE&RC/WIMEK/WASS/EPS/VLAG/WIAS PhD candidates with approved TSP and WU EngD candidates | € 225,- |
| PE&RC postdocs and staff | € 450,- |
| All other academic participants | € 490,- |
| Non-academic participants | € 940,- |
1 The course fee includes a reader, coffee/tea, and lunches. It does not include accommodation .
Note:
PE&RC Cancellation Conditions
IMPORTANT: ALWAYS read the Cancellation conditions for PE&RC courses and activities.
- Dr. Gerrit Gort (Lecturer)
Phone: +31 (0) 317 483570
Email: gerrit.gort@wur.nl
- PE&RC Office
Email: office.pe@wur.nl
