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.
Miguel dos Santos Fonseca
Weekly - 4
Total - Available soon
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.
11. Turkman, M., Paulino, C., Müller, P. (2019). Computational Bayesian Statistics, Cambridge
12. Paulino, C., Turkman, M., Murteira, B., Silva, G. (2018). Estatística Bayesiana. Gulbenkian
The evaluation will be done in 2 moments:
Individual assignment (50% of the grade)
Start - April 17, 2020
Submission - May 3, 2020
Final individual work (50% of grade) -TBA
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
Programs where the course is taught: