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September 5th, 2016, 10:32 AM  #1 
Newbie Joined: Sep 2016 From: US Posts: 1 Thanks: 0  NeymanPearson criterion for two univariate normal distributions
Hi,, I need help in solving this problem.Kindly provide me a way or procedure on how to solve it.No solution reqd Consider the NeymanPearson criterion for two univariate normal distributions: p(xj!i) N(i; 2 i ) and P(!i) = 1=2 for i = 1; 2. Assume a zeroone error loss, and for convenience let 2 > 1. (a) Suppose the maximum acceptable error rate for classifying a pattern that is actually in !1 as if it were in !2 is E1. Determine the singlepoint decision boundary in terms of the variables given. (b) For this boundary, what is the error rate for classifying !2 as !1? (c) What is the overall error rate under zeroone loss? (d) Apply your results to the specic case p(xj!1) N(1; 1) and p(xj!2) N(1; 1) and E1 = 0:05. (e) Compare your result to the Bayes error rate (i.e., without the NeymanPearson conditions). Kindly provide me a way or procedure on how to solve it.No solution reqd 

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criterion, distributions, neymanpearson, normal, univariate 
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