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Suppose that Xi, X2, , xn is an iid sample from a U(0,0) distribution, where θ 0. În turn, the parameter 0 is best regarded as a random variable with a Pareto(a, b) distribution, that is, bab 0, otherwise, where a 〉 0 and b 〉 0 are known. (a) Turn the Bayesian crank to find the posterior distribution of θ. I would probably start by working with a sufficient statistic (b) Find the posterior mean and use this as a point estimator of θ (c) Can θΒ be written as a linear combination of the prior mean and the MLE of θ? If so, prove it. If not, show that this can not be done.

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