Introduction to Bayesian Analysis Procedures |

Textbooks |

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.

DeGroot, M. H. and Schervish, M. J. (2002), *Probability and Statistics*, Reading, MA: Addison Wesley.

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

Ghosh, J. K., Delampady, M., and Samanta, T. (2006), *An Introduction to Bayesian Analysis,* New York: Springer-Verlag.

Lee, P. M. (2004), *Bayesian Statistics: An Introduction,* 3rd ed. London: Arnold.

Sivia, D. S. (1996), *Data Analysis: A Bayesian Tutorial,* Oxford: Oxford University Press.

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. (2006), *Data Analysis Using Regression and Multilevel/Hierarchical Models*, Cambridge: Cambridge University Press.

Goldstein, M. and Woof, D. A. (2007), *Bayes Linear Statistics: Theory and Methods*, New York: John Wiley & Sons.

Harney, H. L. (2003), *Bayesian Inference: Parameter Estimation and Decisions*, New York: Springer-Verlag.

Leonard, T. and Hsu, J. S. (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.

Berger, J. O. (1985), *Statistical Decision Theory and Bayesian Analysis*, New York: Springer-Verlag.

Bernardo, J. M. and Smith, A. F. M. (2007), *Bayesian Theory*, 2nd ed. New York: John Wiley & Sons.

de Finetti, B. (1992), *Theory of Probability*, New York: John Wiley & Sons.

Jeffreys, H. (1998), *Theory of Probability*, Oxford: Oxford University Press.

O’Hagan, A. (1994), *Bayesian Inference*, volume 2B of *Kendall’s Advanced Theory of Statistics,* London: Arnold.

Savage, L. J. (1954), *The Foundations of Statistics*, New York: John Wiley & Sons.

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

Congdon, P. (2006), *Bayesian Statistical Modeling*, 2nd ed. New York: John Wiley & Sons.

Congdon, P. (2003), *Applied Bayesian Modeling*, New York: John Wiley & Sons.

Congdon, P. (2005), *Bayesian Models for Categorical Data*, New York: John Wiley & Sons.

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

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

Copyright © 2009 by SAS Institute Inc., Cary, NC, USA. All rights reserved.