ECTS
5 credits
Component
Faculty of Science
Description
This course provides an introduction to parametric Bayesian statistics. After a presentation of the Bayesian paradigm, the cases of point and ensemble estimation will be considered, followed by the methodology of Bayesian model selection. Binomial, Gaussian and linear models will serve as illustrations for the previous topics.
For complex models, the problems of model estimation and selection in the Bayesian context require the use of advanced integral approximation tools. The second part of the course will therefore focus on Monte Carlo methods and Markov Chain Monte Carlo algorithms.
Objectives
Provide the main tools of Bayesian statistics. Be able to implement them numerically.
Necessary prerequisites
M1 level course in probability and inferential statistics.
Recommended prerequisites: M1 in statistics
Further information
Timetable:
CM: 21h
TD:
TP:
Field :