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Unformatted text preview: θ ). (b) If the loss function is [ w ( y )θ ] 2 ﬁnd the Bayesian point estimate w ( y ) of θ . (c) Show that this w ( y ) is a weighted average of the maximum likelihood estimate y/n and the prior mean αβ , with respective weights n/ ( n + 1 /β ) and (1 /β )( n + 1 /β ). 3. (7.24) Consider a random sample X 1 ,...,X n from a distribution with p.d.f. f ( x  θ ) = 3 θx 2 eθx 3 , < x < ∞ Let θ have a prior p.d.f. which is gamma with α = 4 and β = 1 / 4. Find the conditional mean of θ , given X 1 = x 1 ,...,X n = x n . 1...
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This note was uploaded on 10/05/2009 for the course STATS 620202 taught by Professor R during the One '09 term at University of Melbourne.
 One '09
 R
 Statistics

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