In Example 12.5.6, we modeled the parameters 1, . . . , p as i.i.d. having the
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a. Write the product of the likelihood and the prior as a function of the parameters μ1, . . . , μp, τ1, . . . , τp, ψ, and β.
b. Find the conditional distributions of each parameter given all of the others. For all the parameters besides β, the distributions should be almost identical to those given in Example 12.5.6. Wherever β0 appears, of course, something will have to change.
c. Use a prior distribution in which α0 = λ0 = 1, u0 = 0.001, ∊0 = 0.3, φ0 = 3.0, and ψ0 = 170. Fit the model to the hot dog calorie data from Example 11.6.2. Compute the posterior means of the four μi’s and 1/τi’s. Distribution
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Related Book For
Probability And Statistics
ISBN: 9780321500465
4th Edition
Authors: Morris H. DeGroot, Mark J. Schervish
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