• 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.

Read more

Objectives

Provide the main tools of Bayesian statistics. Be able to implement them numerically.

Read more

Necessary prerequisites

M1 level course in probability and inferential statistics.




Recommended prerequisites: M1 in statistics

Read more

Further information

Timetable:
CM: 21h
TD:
TP:
Field :

Read more