ECTS
5 credits
Component
Faculty of Science
Description
This course provides an introduction to Bayesian parametric statistics. After the presentation of the Bayesian paradigm, the cases of point and set estimates will be considered and the methodology of Bayesian model selection will be discussed. Binomial, Gaussian and linear models will be used to illustrate the previous topics.
For complex models, the problems of estimation and model selection in the Bayesian context require the use of advanced integral approximation tools. Therefore, the second part of the course will 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 pre-requisites
M1 level course in probability and inferential statistics.
Recommended prerequisites: M1 in statistics
Additional information
Hourly volumes:
CM: 21h
TD:
TP:
Field: