### Generate posterior predictive datasets y

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4.3 Posterior predictive checks: Let's investigate the adequacy of the Poisson model for the tumor count data. Following the example in Section

4.4, generate posterior predictive datasets y (1) A , . . . , y (1000) A . Each y (s) A is a sample of size nA = 10 from the Poisson distribution with parameter ? (s) A , ? (s) A is itself a sample from the posterior distribution p(?A|yA), and yA is the observed data. a) For each s, let t (s) be the sample average of the 10 values of y (s) A , divided by the sample standard deviation of y (s) A . Make a histogram of t (s) and compare to the observed

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