| Center |
| Faculty of Mathematics |
| Departament |
| Statistics and Operational Research |
| Lecturers in charge |
| Sin datos cargados |
| Met. Docent |
| Lectures, laboratory work and tutotials |
| Met. Avaluació |
| Final examination: Basic theroretical questions and problems to solve |
| Bibliografia |
| Basic reference Bernardo, J. M. (2005). Reference analysis. Handbook of Statistics 25 (D. K. Dey and C. R. Rao eds.) Amsterdam: Elsevier, 17--90. Additional references: Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis (2nd. ed). Springer-Verlag. Bernardo, J. M. and Smith, A. F. M. (1994). Bayesian Theory. Chichester: Wiley. Gelman, A., Carlin, J. B., Stern, H. and Rubin, D.B. (1995). Bayesian Data Analysis. Chapman and Hall. Lee, P. M. (1989). Bayesian Statistics: An Introduction (2nd. ed). Arnold. O'Hagan, A. (1994). Kendall's Advance Theory of Statistics, Vol. 2B: BayesianInference. Wiley. |
| Continguts |
| Theory programme 1-Foundations. 2-Modeling and the learning process. 3-Inference and prediction 4- Intrinsic loss functions 5-Point estimation. 6-Region estimation. 7-Hypothesis testing. Practical programme Students will solve problems and fully address simple case studies, requiring both analytical and numerical solutions, with the aid of Mathematica |
| Objetius |
| To provide students with a global overview of Bayesian paradigm. To describe its foundations and simple methodological tools; In particular, emphasize objective Bayesian methods. To provide students with the needed tools to solve not too complex inferential tasks from a Bayesian point of view. |
| URL de Fitxa |
| www.uv.es/bernardo/ensenanza.html |