Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
4 credits
15.0 h + 5.0 h
Q2
Teacher(s)
Lambert Philippe;
Language
English
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
At the end of this learning unit, the student is able to : | |
| 1 | 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.
Bibliography
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.
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
LSBA
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Approfondissement en statistique et sciences des données
Master [120] in Biomedical Engineering
Master [120] in Data Science : Statistic
Master [120] in Mathematical Engineering
Master [120] in Data Science Engineering
Master [120] in Statistic: Biostatistics
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Master [120] in Data Science: Information Technology
Master [120] in Economics: General
Master [120] in Mathematics
Master [120] in Statistic: General