This repository centralizes slides, exercises, and supporting scripts for the Applied Bayesian Methods short course.
| Location | AgroParisTech, 14 rue Girardet, Nancy |
| Dates | 7–9 April 2026 |
| Registration deadline | 30 March 2026 |
| Keywords | Statistics, Modelling, Spatio-temporal, Mixed-effects, Ensemble approaches |
By the end of this course, participants will be able to:
- Understand the Bayesian framework and its advantages
- Specify, fit, and interpret hierarchical and mixed Bayesian models
- Incorporate spatial and temporal dependencies into models
- Evaluate, compare, and validate Bayesian models
- Understand and apply ensemble modelling approaches
- Apply Bayesian tools to their own research data
- Report results transparently and reproducibly
- Theoretical background and introduction to the Bayesian framework
- Fitting and interpreting Bayesian mixed effect models
Software: R (rstanarm, brms)
Instructor: Alexander MASSEY → /Day1
- Space and time modelling concepts and approaches in the Bayesian framework
- Fitting and evaluating models with explicit space and/or time components
Software: R (INLA)
Instructor: Lionel HERTZOG → /Day2
- Introduction to ensemble modelling methods with a focus on Bayesian Model Averaging
- Applying and evaluating Bayesian Model Averaging on real-world models
Software: Python
Instructor: Nikola BESIC → /Day3
- Basic statistical concepts (means, correlation, distributions, probability) and tools (regression, correlation)
- Working knowledge of at least one programming language (R, Python, or equivalent)
- Short presentations covering major conceptual knowledge
- Simulated and real-world case studies
- Live coding sessions (R and Python)
- Hands-on practical work in small groups
/day1/ # Day 1 slides, scripts, and exercises (R)
/day2/ # Day 2 slides, scripts, and exercises (R / INLA)
/day3/ # Day 3 slides, scripts, and exercises (Python)
README.md