4.0 credits
15.0 h + 5.0 h
2q
Teacher(s)
Lambert Philippe ;
Language
Français
Main themes
- The Bayesian model: basic principles.
- The likelihood function and its a priori specification.
- One-parameter models: choice of the a priori distribution, derivation of
the a posteriori distribution, summarizing the a posteriori distribution.
- Multi-parameter models: choice of the a priori distribution, derivation
of the a posteriori distribution, nuisance parameters. Special
cases: the multinomial and the multivariate Gaussian models.
- Large sample inference and connections with asymptotic frequentist
inference.
- Bayesian computation.
Aims
By the end of the course, the student will be familiar with the principles
and the basic techniques in Bayesian statistics. He or she will be able to
use and to put forward the advantages and drawbacks of that paradigm in
standard problems.
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
- The Bayesian model: basic principles.
- The likelihood function and its a priori specification.
- One-parameter models: choice of the a priori distribution, derivation of
the a posteriori distribution, summarizing the a posteriori distribution.
- Multi-parameter models: choice of the a priori distribution, derivation
of the a posteriori distribution, nuisance parameters. Special
cases: the multinomial and the multivariate Gaussian models.
- Large sample inference and connections with asymptotic frequentist
inference.
- Bayesian computation.
Other information
References :
Ouvrages de référence
Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B. (2003,2nd edition) Bayesian Data Analysis. Chapman and Hall.
Spiegelhalter, D.J., Thomas, A. and Best, N.G. (1999) WinBUGS User Manual. MRC Biostatistics Unit.
Bolstad, W.M.(2004) Introduction to Bayesian Statistics. Wiley.
Faculty or entity<
Programmes / formations proposant cette unité d'enseignement (UE)
Program title
Sigle
Credits
Prerequisites
Aims
Minor in Statistics
Master [120] in Biomedical Engineering
Master [120] in Economics: General
Master [120] in Mathematical Engineering
Master [120] in Biomedicine
Master [120] in Statistics: General
Additionnal module in Mathematics
Master [120] in Statistics: Biostatistics
Master [120] in Mathematics
Master [120] in Business Engineering
Master [120] in Business Engineering