Introduction to Bayesian Analysis Procedures
Textbooks
Introductory Books
Berry, D. A. (1996). Statistics: A Bayesian Perspective. Belmont, CA: Duxbury Press.
Bolstad, W. M. (2007). Introduction to Bayesian Statistics. 2nd ed. Hoboken, NJ: John Wiley & Sons.
DeGroot, M. H., and Schervish, M. J. (2002). Probability and Statistics. 3rd ed. Reading, MA: Addison-Wesley.
Gamerman, D., and Lopes, H. F. (2006). Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference. 2nd ed. Boca Raton, FL: Chapman & Hall/CRC.
Ghosh, J. K., Delampady, M., and Samanta, T. (2006). An Introduction to Bayesian Analysis: Theory and Methods. New York: Springer-Verlag.
Lee, P. M. (2004). Bayesian Statistics: An Introduction. 3rd ed. London: Edward Arnold.
Sivia, D. S. (1996). Data Analysis: A Bayesian Tutorial. Oxford: Oxford University Press.
Intermediate-Level Books
Box, G. E. P., and Tiao, G. C. (1992). Bayesian Inference in Statistical Analysis. New York: John Wiley & Sons.
Chen, M.-H., Shao, Q.-M., and Ibrahim, J. G. (2000). Monte Carlo Methods in Bayesian Computation. New York: Springer-Verlag.
Gelman, A., and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press.
Goldstein, M., and Wooff, D. A. (2007). Bayes Linear Statistics: Theory and Methods. Chichester, UK: John Wiley & Sons.
Harney, H. L. (2003). Bayesian Inference: Parameter Estimation and Decisions. Berlin: Springer-Verlag.
Leonard, T., and Hsu, J. S. J. (1999). Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers. Cambridge: Cambridge University Press.
Liu, J. S. (2001). Monte Carlo Strategies in Scientific Computing. New York: Springer-Verlag.
Marin, J.-M., and Robert, C. P. (2007). Bayesian Core: A Practical Approach to Computational Bayesian Statistics. New York: Springer-Verlag.
Press, S. J. (2002). Subjective and Objective Bayesian Statistics: Principles, Models, and Applications. 2nd ed. New York: Wiley-Interscience.
Robert, C. P. (2001). The Bayesian Choice. 2nd ed. New York: Springer-Verlag.
Robert, C. P., and Casella, G. (2004). Monte Carlo Statistical Methods. 2nd ed. New York: Springer-Verlag.
Tanner, M. A. (1993). Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions. New York: Springer-Verlag.
Advanced Titles
Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis. 2nd ed. New York: Springer-Verlag.
Bernardo, J. M., and Smith, A. F. M. (2007). Bayesian Theory. 2nd ed. Chichester, UK: John Wiley & Sons.
De Finetti, B. (1992). Theory of Probability: A Critical Introductory Treatment. Chichester, UK: John Wiley & Sons.
Jeffreys, H. (1998). Theory of Probability. 3rd ed. Oxford: Oxford University Press.
O’Hagan, A. (1994). Bayesian Inference. Volume 2B of Kendall’s Advanced Theory of Statistics. London: Edward Arnold.
Savage, L. J. (1954). The Foundations of Statistics. New York: John Wiley & Sons.
Books Motivated by Statistical Applications and Data Analysis
Carlin, B. P., and Louis, T. A. (2000). Bayes and Empirical Bayes Methods for Data Analysis. 2nd ed. London: Chapman & Hall.
Congdon, P. (2003). Applied Bayesian Modeling. New York: John Wiley & Sons.
Congdon, P. (2005). Bayesian Models for Categorical Data. Chichester, UK: John Wiley & Sons.
Congdon, P. (2006). Bayesian Statistical Modeling. 2nd ed. New York: John Wiley & Sons.
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2004). Bayesian Data Analysis. 2nd ed. London: Chapman & Hall.
Gilks, W. R., Richardson, S., and Spiegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice. London: Chapman & Hall.
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