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- Markov Chain Monte Carlo (MCMC):
- Chen, M.-H, Ibrahim, J.G. and Shao, Q.-M. (2000). Monte Carlo
Methods in Bayesian Computation, Springer, New York.
- Gilks, W.R., Richardson, S. and Spiegelhalter, D. J. (1996).
Markov Chain Monte Carlo in Practice, Chapman & Hall, London.
- WinBUGS:
Spiegelhalter, D.J., Thomas, A. and Best, N. G. (1999). WinBUGS
Version 1.3 User Manual MRC Biostatistics Unit, Cambridge.
- Bayesian Nonparametrics:
- Speckman, P. and Sun, D. (2003). Fully Bayesian
spline smoothing and intrinsic autoregressive priors, Biometrika, 90, 289-302.
- Ghosh, J. K. and Ramamoorthi, R. V. (2003) Bayesian Nonparamterics, Springer-Verlag, New York.
- Ishwaran, H. and Zarepour, M. (2002). Dirichlet prior sieves in finite normal mixtures, Statistica Sinica, 12, 941-963.
- Ishwaran, H. and James, L. F. (2002).
Approximate Dirichlet process computing in finite normal mixtures: smoothing and prior
information, Journal of Computational Graphical Statistics, 11, 508-532.
- Neal, R. M. (1998).
Markov chain sampling methods for Dirichlet process mixture models, Technical Report No. 9815, Dept. of Statistics, University of Toronto.
- Wahba, G. (1978). Improper Priors, Spline Smoothing and the Problem of Guarding
Against Model Errors in Regression, JRSS-B, 40, 364-372.
- Bayesian Variable Selection Methods:
- Barbieri, and Berger, J. O. (2003). Optimal predictive model selection, ISDS Discussion paper 02-02,
Duke University.
- Berger, J. O. and Pericchi, L. R. (2001). Objective Bayesian methods for model selection: introduction
and comparison (with discussion), Model Selection (ed. Lahiri), IMS Lecture Notes, 38, 135-207.
- Brown, P. J., Vannucci, M. and Fearn, T. (1998). Multivariate Bayesian variable selection and prediction,
Journal of the Royal Statistical Society B, 60, 627-641.
- Casella, G. and Moreno, E. (2006). Objective Bayesian variable selection, Journal of the American Statistical
Association, 101, 157-167.
- Chipman, H. (1996). Bayesian variable selection and related predictors, Canadian Journal of Statistics,
24, 17-36.
- Foster, D. P. and George, E. I. (1994). The risk inflation criterion for multiple regression, Annals
of Statistics, 22, 1947-1975.
- Gelfand, A. E. and Ghosh, S. K. (1998). Model choice: a minimum posterior predictive loss approach,
Biometrika, 85, 1-11.
- George, E. (2002). The variable selection problem, Statistics in the 21st Century (eds
Raftery, Tanner and Wells), 350-358.
- George, E. I. and Foster, D. P. (2000). Calibration and empirical Bayes variable selection,
Biometrika, 87, 731-747.
- George, E. I. and McCulloch, R. E. (1993). Variable selection via Gibbs sampling, Journal of the
American Statistical Association, 88, 881-889.
- George, E. I. and McCulloch, R. E. (1995). Stochastic search variable selection, Markov Chain
Monte Carlo in Practice (eds Gilks, Richardson and Spiegelhalter), 203-214.
- George, E/ I. and McCulloch, R. E. (1997). Approaches for Bayesian variable selection, Staistica
Sinica, 7, 339-373.
- Geweke, J. (1996). Variable selection and model comparison in regression, Bayesian Statistics
(eds. Bernardo, Berger, Dawid and Smith), 5, 609-620.
- Lindley, D. V. (1968). The choice of variables in multiple regression (with discussion), Journal
of the Royal Statistical Society B, 30, 31-66.
- Mitchell, T. J. and Beauchamp, J. J. (1988). Bayesian variable selection in linear regression (with discussion),
Journal of the American Statistical Association, 83, 1023-1036.
- O'Hagan, A. (1995). Fractional Bayes factors for model comparison (with discussion), Journal of the
Royal Statistical Society B}, 57, 99-138.
- Smith, M. and Kohn, R. (1996). Nonparametric regression using Bayesian variable selection, Journal
of Econometrics, 75, 317-344.
- Smith, A. F. M. and Spiegelhalter, D. J. (1980). Bayes factors and choice criteria for linear models,
Journal of the Royal Statistical Society B, 42, 213-220.
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