Graduate Program

__Credit hours__

In-class work per week |
Practice per week |
Credits |
Duration |
Total |

2 |
2 |
8 |
15 weeks |
120 hours |

__Instructor__

Afrânio Márcio Corrêa Vieira

Cristian Marcelo Villegas Lobos

Marcelo Andrade da Silva

__Objective__

Disseminate the Bayesian approach among students and researchers and enable them in their ability to analyze data through Bayesian inference statistical models.

__Content__

Basic concepts in Bayesian methods (methods of statistical inference, Bayes Theorem, Introduction to Bayesian inference, distribution a priori, methods of sampling Markov Chain Monte Carlo, STAN software); Tools for modeling (Bayesian Hierarchical Models, pets and choice of model); Bayesian methods in practical applications (biossay, longitudinal analysis).

__Bibliography__

Berry, D. A. (1996), Statistics: A Bayesian Perspective, London: Duxbury Press.

Bolstad, W. M. (2007), Introduction to Bayesian Statistics, 2nd ed. New York: John Wiley & Sons.

Box, G. E. P.; Tiao, G. C. (1992), Bayesian Inference in Statistical Analysis, New York: John Wiley & Sons.

Carlin, B. ; Louis, T. A. (2000), Bayes and Empirical Bayes Methods for Data Analysis, 2nd ed. London: Chapman & Hall.

Chen, M. H.; Shao Q. M.; Ibrahim, J. G. (2000), Monte Carlo Methods in Bayesian Computation, New York: Springer-Verlag.

Lesaffre, E.; Lawson, A. B. (2012), Bayesian Biostatistics. London: Wiley.

Gamerman, D.; Lopes, H. F. (2006), Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, 2nd ed. London: Chapman & Hall/CRC.

Gelman, A.; Carlin, J. B.; Stern, H. S.; Rubin, D. B. (2004), Bayesian Data Analysis, 3rd ed. London: Chapman & Hall.

Gilks, W. R.; Richardson, S.; Spiegelhalter, D. J. (1996), Markov Chain Monte Carlo in Practice, London: Chapman & Hall.

McElreath, R. (2015), Statistical Rethinking: A Bayesian Course with Examples in R and Stan, London:CRC Press.

Migon H. S.,Gamerman D. ; Louzada, F. (2015), Statistical Inference: An Integrated Approach, 2nd ed. London: CRC Press.

Robert, C. P. ; Casella, G. (2004), Monte Carlo Statistical Methods, 2nd ed. New York: Springer-Verlag.