LCE5813 - Introduction to Bayesian Inference

Credit 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.