Lecturer: Guido Consonni (firstname.lastname@example.org)
Tutor: Roberto De Monte
The course will be devoted to Bayesian inference and modeling: this is an approach to Statistics which has been growing very rapidly over the past decades and should be known by researchers, especially in the applied domain.
Prior, posterior, predictive distributions.
Inference and prediction for standard models: Bernoulli, Normal, Poisson.
Regression models and elements of Bayesian econometrics.
Computational methods: Markov Chain Monte Carlo (MCMC).
The course will make extensive use of the software R, a powerful freeware language. We will provide tutorials on the R software so as to make students sufficiently proficient.