Checking date: 28/04/2022

Course: 2022/2023

Bayesian Methods
Bachelor in Statistics and Business (Plan: 400 - Estudio: 203)

Coordinating teacher: WIPER , MICHAEL PETER

Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Statistical Inference Techniques I Statistical Inference Techniques II Regression Methods Stochastic Processes
1. To understanding the ideas of Bayesian statistics and the differences between this approach and the classical or frequentist approach in Statistics. 2. To know and use the main conjugate families of distributions. 3. Use specific Bayesian statistical software to solve problems. 1. Capacity for analysis and synthesis. 2. Model and solve problems. 3. Oral and written communication skills.
Skills and learning outcomes
Description of contents: programme
1. Introduction and review of basic concepts of probability theory. 1.1 Definitions and basic theorems 1.2 Bayes theorem 1.3 Applications of the Bayes thorem 2. Conjugate families of distributions. 2.1 Beta-binomial family 2.2 Normal-normal family 2.3 Applications 3. Estimation and tests. 3.1 Beta-binomial models 3.2 Normal-normal models 3.3 Examples 4. Regression and linear models. 4.1 Normal linear models 4.2 General linear models 5. Simulation methods for Bayesian statistics. 5.1 Bayes factors 5.2 Introduction to MCMC methods 5.3 Examples
Learning activities and methodology
Theory (4 ECTS). Theoretical classes with support material available on the Web. Practice (2 ECTS) problem-solving classes. Computing practices in computer labs. Presentations and debates.
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
Calendar of Continuous assessment
Basic Bibliography
  • Antelman, G.. Elementary Bayesian Statistics. Cheltenham. 1997
  • Bernardo, J.M.. Bioestadística una perspectiva Bayesiana. Vicens Viven, España. 1981
  • Boldstad, W.M.. Introduction to Bayesian Statistics. Wiley. 2007
  • Gill J.. Bayesian Methods: A Social and Behavioral Sciences Approach (3ed). Chapman & Hall. . 2015
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Albert J.. Bayesian Computation with R (Use R). Springer. 2009
  • Lee, P.M.. Bayesian Statistics: An Introduction. Arnold, Londres. 2004
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus may change due academic events or other reasons.

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