Titlebook: Applied Bayesian Statistics; With R and OpenBUGS Mary Kathryn Cowles Textbook 2013 Springer Science+Business Media New York 2013 Bayesian.

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Special Considerations in Bayesian Inference, as Bayesians must worry about robustness of their inferences. One concern for both camps is that assumptions regarding the form of the likelihood can affect inference. For example, suppose that our data are scores obtained by 100 undergraduates on a calculus exam, and that we wish to use these data

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Other One-Parameter Models and Their Conjugate Priors,s a count of the number of rare events occurring per unit time, unit volume, unit distance, etc. For example, the number of new cases of rhabdomyosarcoma (a rare form of cancer) occurring in Johnson County, Iowa, each year might be represented as a Poisson random variable.

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Hierarchical Models and More on Convergence Assessment,ability distribution, and the prior, which specifies a probability distribution on the unknown parameters in the likelihood. Such a simple model is inadequate for many (probably most) real-world applications. As a result, more complex Bayesian models with additional levels are very commonly used. Su

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Model Comparison, Model Checking, and Hypothesis Testing,riteria for determining which of the candidate models is best, and whether even that model is good enough to use as the basis for inference. This chapter considers Bayesian methods of comparing models, testing hypotheses, and assessing model adequacy.

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