Bayesian Analysis
A.Y. 2021/2022
Learning objectives
The aim of the course is to introduce the Bayesian approach to statistical inference. The course will develop the relevant methodology, theory and computational techniques necessary to its implementation. In the course single and multi-parameter models will discussed.
Expected learning outcomes
At the end of the course students will know Bayesian inference and the computational methodology necessary to its implementation.
Lesson period: Second trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second trimester
More specific information on the delivery modes of training activities for academic year 2021/22 will be provided over the coming months, based on the evolution of the public health situation.
Course syllabus
The course will cover the following topics: general idea of Bayesian analysis (including Bayes theorem for distributions); prior and posterior distributions (conjugate priors); different levels of prior knowledge; asymptotic posterior distribution; Bayesian inference (point estimates and intervals estimates); prediction; inference for the normal distribution; asymptotic posterior for the multiparameter case; Markov Chain Monte Carlo (MCMC) techniques for non-conjugate analysis.
Prerequisites for admission
Good basis of mathematics (differentiation, integration). Good knowledge of classical statistics (random variable, probability, central limit theorem, point and interval estimation).
Teaching methods
Teaching will mainly be delivered through lectures and exercise classes.
Teaching Resources
Electronic copies of slides and lecture notes will be made available as the course unfolds. Reference will be communicated during the first lecture.
Assessment methods and Criteria
Written final exam.
Professor(s)
Reception:
Each Wednesday 12-14
DEMM, room 31, 3° floor (By appointment, please send an email)