Checking date: 02/04/2019


Course: 2019/2020

Bayesian Inference
(17763)
Study: Master in Statistics for Data Science (345)
EPI


Coordinating teacher: CABRAS , STEFANO

Department assigned to the subject: Department of Statistics

Type: Compulsory
ECTS Credits: 3.0 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. Concepts of proability relateded with Bayesian Statistics 2. Conjugate families of distributions 3. Subjective and objective prior distributions 4. Numerical methods y MCMC a) analytic approximations b) Monte Carlo c) MCMC and Gibbs sampling 5. Estimation and hypothesis testing 6. Regression and hierarchical models 7. Time series and forecasting
Learning activities and methodology
Practical sessions on Bayesian computing and on the use of Bayesian software to implement MCMC algorithms. Classes will be mainly oriented at practicing and verify concepts illustrated in the corresponding book chapters. It is up to the student to arrive at class with all these concepts already understood and studied. This is the only activity demanded out of the class. This is in a coherent realization of the flipped classroom methodology.
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
Basic Bibliography
  • Jeff Gill. Bayesian Methods A Social and Behavioral Sciences Approach Third Edition . CRC Press.
Additional Bibliography
  • Bolstad, W.M.. Introduction to Bayesian statistics. Wiley.
  • Box, G.E. and Tiao, G.C.. Bayesian inference in statistical analysis. Wiley.
  • Chen, M-H. Monte Carlo methods in bayesian computation. Springer.
  • Congdon, P.. Applied Bayesian modelling. Wiley.
  • D' Agostini, J.. Bayesian reasoning in data analysis : a critical introduction. World Scientific.
  • Dey, D.K. and Rao, C.R.. Bayesian thinking : modeling and computation. Elsevier.
  • Gamerman, D.. Markov chain Monte Carlo : stochastic simulation for Bayesian inference. Chapman & Hall.
  • Gilks, W., Richardson, S. and Spiegelhalter, D.J.. Markov chain Monte Carlo in practice. Chapman and Hall.
  • Robert, C.P.. The Bayesian choice : from decision-theoretic foundations to computational implementation (2nd edition). Springer.

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


More information: http://portal.uc3m.es/portal/page/portal/dpto_estadistica/personal/Stefano_Cabras