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CASt online resources

Baysian methods
Bayesian Data Analysis
    by Andrew Gelman, John Carlin, Hal Stern & Donald Rubin (2nd ed, 2003). Well-respected and popular graduate textbook on Bayesian inference and modelling. Includes single- and multi-parameter models, large-sample inference, hierarchical models, sensitivity analysis, experiment design, regression models, mixture models, missing data, and computational issues (EM algorithm, posterior simulation, Metropolis algorithm, Gibbs sampler).

Bayesian Logical Data Analysis for the Physical Sciences
    by P. C. Gregory (2005). The first monograph by an astronomer on Bayesian statistical methods. Covers the elements of Bayesian inference, maximum entropy probabilities, Markov chain Monte Carlo, and Bayesian approaches to spectral analysis (e.g. periodic time series) and Poisson sampling.

Data Analysis: A Bayesian Tutorial
    by Devinderjit S. Sivia & John Skilling (2nd ed, 2006). Introduction Bayesian methodology for scientists and engineers. Includes parameter estimation, model selection, nonparametric estimation, experimental design, least-squares extensions, error propagation, nested sampling, and image processing.

Statistical Decision Theory and Bayesian Analysis
    by James O. Berger (2nd ed, 1985).  Authoritative and popular graduate-level textbook on Bayesian methodology.  Covers concepts (loss function, prior information, Bayesian inference), empirical Bayes, hierarchical Bayes, robustness, computation, minimax methods, invariance, sequential analysis, complete classes.

Markov Chain Monte Carlo in Practice
    by W. R. Gilks, S. Richardson & D. J. Spiegelhalter (1996).  A multi-author discussion of MCMC computational issues in Bayesian modeling, with applications in many fields (including astronomy). Topics include Markov chain concepts, state-space modeling, computational strategies & convergence, hypothesis testing and model selection, model improvement, variable selection, EM algorithm, hierarchical & nonlinear models, image analysis, measurement error, mixture models.

Bayes and Empirical Bayes Metehods for Data Analysis
    by Bradley P. Carlin & Thomas A. Louis (2nd ed, 2000). Graduate-level volume on Bayesian computation with examples from biomedicine and WinBUGS software. Topics include an introduction to Bayesian inference, empirical Bayes approach, comparison with frequentist procedures, MCMC computation, model criticism & selection, decision theory, and special cases.
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