Description:

The course will present the basic aspects of Bayesian Statistics, like:

-Prior elicitation, Bayes Theorem, posterior distributions

-Inference (point and interval estimation)

-Robustness

-Markov Chain Monte Carlo

-Hypothesis testing (mixture models and Bayes factor)

-Hierarchical Models

-Regression (linear and, time allowing, logistic)

-Inference for stochastic processes (Markov chains and, time allowing, Poisson processes)

The course will present also some case studies analyzed by the lecturer and the R software will be used for some simple computations.

Slides: 

 CINA24-01.pdf

 CINA24-02.pdf

 CINA24-R-02.pdf

 CINA24-R-03.pdf

 CINA24-03.pdf

 cina24-walmart.pdf

 CINA24-R-04.pdf

 CINA24-04.pdf

 CINA24-R-05.pdf

 CINA24-05.pdf

 R-intro.pdf

 CINA24-06.pdf

Prerequisite:

Introductory course on (Frequentist) Statistics and, possibly, Probability

Reference:

Lecturer's notes

Albert (2009), Bayesian Computation with R, Springer

Rios Insua, Ruggeri, Wiper (2012), Bayesian Analysis of Stochastic Process Models

Target Audience: Undergraduate students (Main target), Graduate students (Welcome!)

Teaching Language: English

Registration: https://www.wjx.top/vm/O0QPTdZ.aspx#

Bio:

President-Elect of the International Statistical Institute (ISI), Senior Fellow at the Italian National Research Council, Elected Fellow of ISI and Fellow of American Statistical Association, Institute of Mathematical Statistics and International Society for Bayesian Analysis (which gave him the first Zellner Medal), author of 200+ articles and 6 books.

Recordings: https://archive.ymsc.tsinghua.edu.cn/pacm_course?html=Bayesian_Statistics.html