| Area of Mathematics: | Statistics-Probability-Operational Research (ESPOR) | ||
| Semester: | 7ο | ||
| Course ID: | 72304 | ||
| Course Type: | Elective | ||
| Teaching hours per week: | Theory: 4 | Practice: 0 | Laboratory: 0 |
| ECTS : | 5 | ||
| Eclass: | |||
| Instructors: | |||
Description
- Fundamental principles of Bayesian Statistics. Bayesian Statistics and Probability. Objective and subjective probability, characteristics of Bayesian approach, likelihood principle.
- Prior distribution (conjugate, non informative, improper, Jeffreys- mixed priors)
- Bayesian multivariate analysis.
- Statistical inference: decision theory, Bayes risk, Bayes rule, minimax, point estimation, interval estimation, hypotheses testing, forecasting distribution, asymptotic theory.
- Stochastic simulation. Introduction to MCMC algorithms. Simulation from the posterior distribution. Metropolis-Hastings algorithm. Gibbs sampling. Use of WinBugs package. Methods of model selection
Bibliography
- P. Robert, The Bayesian Choice.
- M. Bernardo and A. F. M. Smith, Bayesian Theory.
- P. Karlin and T. A. Louis, Bayes and Empirical Bayes Methods for Data Analysis.
- O’Hagan and J. Foster, Kendall’s Advanced Theory of Statistics: Volume 2B: Bayesian Inference.
