Titlebook: Bayesian Computation with R; Jim Albert Textbook 20071st edition Springer-Verlag New York 2007 Bayesian Inference.Hierarchical modeling.Ma

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Introduction to Bayesian Computation,simply computes values of the posterior on a grid of points and then approximates the continuous posterior by a discrete posterior that is concentrated on the values of the grid. This brute-force method can be generally applied for oneand two-parameter problems such as those illustrated in Chapters 3 and 4.

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Model Comparison,with a “streaky” model where the probability of a success may change over a season. In the second application, we illustrate the computation of Bayes factors against independence in a two-way contingency table.

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Introduction to Bayesian Thinking,rtion. Before taking data, one has beliefs about the value of the proportion and one models his or her beliefs in terms of a prior distribution. We will illustrate the use of different functional forms for this prior. After data have been observed, one updates one’s beliefs about the proportion by t

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