Bayesian Methods
Code
12080
Academic unit
Faculdade de Ciências e Tecnologia
Department
Departamento de Matemática
Credits
6.0
Teacher in charge
Miguel dos Santos Fonseca
Weekly hours
4
Teaching language
Português
Objectives
The objective of this curricular unit is to learn the Bayesian paradigm in the statistical analysis of data, methodologies and computational techniques for inference, hypothesis testing and prediction.
Subject matter
1 - The Bayesian paradigm
2 - The prior distribution and methods for its formulation
3 - The likelihood function, the posterior distribution, the marginal and predictive distributions
4 - Bayesian inference
5 - Markov Chain Monte Carlo, MCMC
6 - Model evaluation and selection
7 - Hierarchical models
Bibliography
1. Albert, J. (2009). Bayesian Computation with R. Spinger.
2. Bernardo J.M. & Smith, A.F.M. (1994). Bayesian theory. Wiley.
3. Congdon P (2001). Bayesian Statistical Modelling. Wiley.
4. Cowles, M.K. (2013). Applied Bayesian Statistics. With R and OpenBUGS Examples. Springer.
5. Gamerman, D. & Lopes, H.F. (2006). Markov chain Monte Carlo - stochastic simulation for Bayesian inference. 6. Chapman & Hall/CRC.
7. Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B. (2003). Bayesian Data Analysis (2nd edition).
8. Chapman and Hall / CRC, 2003.
9. Gilks, W.R., Richardson, S. and Spiegelhalter, D. (Edts.) (1996) Markov chain Monte Carlo in Practice. Chapman and Hall/CRC.
10. Lee, P.M. (2004). Bayesian Statistics: An Introduction, 3rd edition, Arnold.
Evaluation method
The evaluation will be done in 3 moments:
- Test (30% of the grade) - April 3, 2019
- Individual work (30% of the grade) - TBA
- Final individual work (30% of grade) -TBA