Bayesian Statistics

Fabrizio Ruggeri

（CNR）

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.

Time

Tues. & Thur., 9:50 am-12:15

Sept. 19-26, Oct. 8-24, 2024

Venue

Lecture Hall B725

Shuangqing Complex Building A

Online

Zoom Meeting ID: 271 534 5558

Passcode: YMSC

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.

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!)