Checking date: 30/04/2019


Course: 2019/2020

Bayesian Inference
(15465)
Study: Master in Mathematical Engineering (70)
EPI


Coordinating teacher: MARIN DIAZARAQUE, JUAN MIGUEL

Department assigned to the subject: Department of Statistics

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Students are expected to have completed
Experience with classical statistical methods.
Competences and skills that will be acquired and learning results.
The aim of this course is to introduce the modern approach to Bayesian statistics, emphatizing the computational aspects and the differences between the classical and Bayesian approaches.
Description of contents: programme
1. Introduction & basic results a) probability and Bayes theorem b) coin tossing problems 2. Conjugate families of distributions a) coin tossing problems b) rare events c) problems with the normal distribution 3. Subjective and objective prior distributions a) bayesian inference and subjective priors b) objective priors 4. Numerical methods y MCMC a) analytic approximations b) Monte Carlo c) MCMC and Gibbs sampling 5. Estimation and hypothesis testing a) point and interval estimation b) hypothesis tests and model selection c) Bayes factor and the DIC 6. Regression and hierarchical models a) linear regression b) hierarchical models c) generalized linear models 7. Time series & forecasting a) Dynamic linear models b) Bayesian Kalman filter c) Other models 8. Nonparametrics a) Non-parametric inference b) Dirichlet processes
Learning activities and methodology
Practical sessions on Bayesian computing and on the use of Bayesian software to implement MCMC algorithms.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100

The course syllabus and the academic weekly planning may change due academic events or other reasons.