2 ECTS Kourou Year 2 Semester 3 Elective Tool
Content
This course offers a comprehensive exploration of Bayesian statistics with a focus on ecological applications, emphasizing both theoretical concepts and practical coding skills. Key topics include conjugate priors for analytical simplicity, Markov Chain Monte Carlo (MCMC) techniques for complex models, and hands-on experience coding with JAGS in the R framework. You will also explore hidden hierarchical models, which are essential for understanding latent ecological processes.
Learning outcomes
After completing the course, the student should be able to 1) analyse a dataset, build, write and implement a model with Rstan, 2) interpret the results, and be aware of the limitations of these methods, 3) apply machine learning algorithms adapted to different tasks (classification, segmentation…), and 4) wisely split the data set into test, training and validation sets.
Teaching and learning methods
The course is entirely provided in the classroom (19h).
Type of assessment
- Individual
- Written assignment
A small dataset will be provided to the students and they will have to conduct an analysis on it using the Bayesian framework.
Institution(s)
- Location: Kourou
- ECTS granting: AgroParisTech
- Organisation: AgroParisTech
Webpage(s)
Coordinator(s)
