Bayesian statistics

This course offers an introduction to parametric Bayesian statistics. After presenting the Bayesian paradigm, point and ensemble estimators will be considered, followed by a discussion of Bayesian model selection methodology. Binomial, Gaussian, and linear models will be used to illustrate the above topics.

For complex models, the issues of estimation and model selection in the Bayesian context require the use of advanced tools for approximating integrals. Therefore, the second part of the course will focus on Monte Carlo methods and Markov chain Monte Carlo algorithms.

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

Master's level course in probability and inferential statistics.




Recommended prerequisites: Master's degree in statistics

Hours:
CM: 21 hours
TD:
TP:
Fieldwork: