Bayesian Statistics

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

For complex models, the problems 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 (M1) course in probability and inferential statistics.




Recommended prerequisites: Master's degree (M1) in statistics

Hours:
Lectures: 9:00 p.m.
Tutorials:
Lab:
Fieldwork: