Course detail

LCE5813 - Introduction to Bayesian Inference


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

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 (Approaches to statistical inference, Bayes Theorem, Introduction to
Bayesian Inference, a priori distribution, Stochastic Markov Chain simulation methods, Bayesian
computing languages); Bayesian modeling tools (hierarchical models, estimation and model choice);
Bayesian methods in practical applications (Linear Models, Generalized Linear Models, Bioassay Models,
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.
Congdon, P. (2005), Bayesian Models for Categorical Data, New York: John Wiley & Sons.
Congdon, P. (2006), Bayesian Statistical Modeling, 2nd ed. New York: John Wiley & Sons.
DeGroot, M. H. ; Schervish, M. J. (2002), Probability and Statistics, Reading, MA: Addison Wesley.
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.
Migon H. S.,Gamerman D. ; Louzada, F. (2015), Statistical Inference: An Integrated Approach, 2nd ed.
London: CRC Press.
O’Hagan, A; Buck, C. E.; Alireza D.; Eiser, J. R.; Garthwaite, P. H.; Jenkinson, D. J.; Oakley, J. E.;
Rakow, T. (2006), Uncertain Judgements: Eliciting Experts’ Probabilities.
Paulino, C.D.; Turkman, M.A.A. & Murtera, B. Estatística Bayesiana. Fundação Calouste Gulbenkian –
Lisboa, 2003.
Robert, C. P. ; Casella, G. (2004), Monte Carlo Statistical Methods, 2nd ed. New York: Springer-Verlag.