Summary and Info
Geweke (economics, statistics, U. of Iowa) gives a thorough understanding of Bayesian analysis grounded in the theory of inference and optimal decision-making. He covers the basics of the elements of Bayesian inference, posterior simulation, linear models, modeling with latent variables, modeling for time series and Bayesian investigation, and includes such topics in inference as hierarchical priors and latent variables, improper prior distributions, prior robustness and the density ratio class, asymptotic analysis, and the likelihood principle. Geweke also details how models can be applied to specific problems, including linear models and policy choices, modeling with latent variables and missing data, prediction with time series models, and comparison and evaluation of models.
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