Referências Bibliográficas

Referências básicas

• Carlin, BP; Louis, TA. Bayesian methods for data analysis. Chapman & Hall, 2009.
• Coles, S. (tradução e adaptação de Ribeiro Jr, P.J.) Introdução à Inferência Bayesiana.
• Gelman, A; Carlin, JB; Stern, HS; Dunson, DB; Vehtari, A; Rubin, DB. Bayesian data analysis. Chapman & Hall, 2014.
• Hoff, PD. A first course in Bayesian statistical methods. Springer, 2009.
• Kinas, PG; Andrade, HA. Introdução à análise bayesiana (com R). Consultor Editorial, 2020.
• Migon, H, Gamerman, D, & Louzada, F. Statistical Inference: An Integrated Approach. Chapman & Hall, 2015.
• Paulino, CD; Turkman, MAA; Murteira, B; Silva, GL. Estatística bayesiana. Fundação Calouste Gulbenkian, 2018.
• Stone, JV. Bayes’ Rule: a tutorial introduction to Bayesian analysis. Sebtel Press, 2013.

Referências complementares

• Albert, J. Bayesian computation with R. Springer, 2007.
• Hobbs, NT; Hooten, MB. Bayesian models. A ststistical primer for ecologists. Princeton University Press, 2015.
• Jaynes, ET. Probability theory: the logic of science. Cambridge University Press, 2003.
• Kruschke, JK. Doing bayesian data analysis. Academic Press, 2015.
• Lee, PM. Bayesian ststistics: an introduction. Arnold, 2004.
• McElreath, R. Statistical rethinking. A bayesian course with examples in R and Stan. Chapman & Hall, 2016.
• Robert, CP; Casella, G. Introducing Monte Carlo methods in R. Springer, 2010.
• Sivia, DS; Skilling, J. Data analysis: a Bayesian tutorial. Oxford University Press, 2006.

Referências online

• Gómez-Rubio, V. Bayesian inference with INLA and R-INLA.

Leituras complementares

Leituras relevantes para o tema, mas não necessariamente relacionadas diretamente com a disciplina.

• Jeffreys, H. Scientific inference. Cambridge University Press, 1957.
• Jeffreys, H. Theory of probability. Cambridge University Press, 1948.
• Pólya, G. Mathematics and plausible reasoning. Princeton University Press, 1990.

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