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Unformatted text preview: Utah State University ECE 6010 Stochastic Processes Homework # 11 Solutions 1. Let M n denote the sequence of sample means from an iid random process X n : M n = X 1 + X 2 + ··· + X n n . (a) Is M n a Markov process? M n = 1 n n X i =1 X i = 1 n [ X n + ( n 1) M n 1 ] = 1 n X n + (1 1 n ) M n 1 Clearly if M n 1 is given then M n depends only on X n and is independent of M n 2 , M n 3 , . . . . There fore, M n is a Markov process. (b) If the answer to part a is yes, find the following state transition pdf: f M n ( x  M n 1 = y ). f M n ( x  M n 1 = y ) = P [ x < M n ≤ x + dx  M n 1 = y ] = P [ x < 1 n + (1 1 n ) y ≤ x + dx ] = P [ nx ( n 1) y < x n ≤ nx ( n 1) y + dx ] = f x ( nx ( n 1) y ) dx 2. An urn initially contains five black balls and five white balls. The following experiment is repeated indef initely: A ball is drawn from the urn; if the ball is white it is put back in the urn, otherwise it is left out. Let X n be the number of black balls remaining in the urn after n draws from the urn. (a) Is X n a Markov process? If so, find the apropriate transition probabilities. (b) Do the transition probabilities depend on n ? The number X n of black balls in the urn completely specifies the probability of the outcomes of a trial; therefore X n is independent of its past values and X n is a Markov proces. P [ X n = 4  X n 1 = 5] = 5 10 = 1 P [ X n = 5  X n 1 = 5] P [ X n = 3  X n 1 = 4] = 4 9 = 1 P [ X n = 4  X n 1 = 4] P [ X n = 2  X n 1 = 3] = 3 8 = 1 P [ X n = 3  X n 1 = 3] P [ X n...
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This note was uploaded on 03/01/2012 for the course ECE 6010 taught by Professor Stites,m during the Spring '08 term at Utah State University.
 Spring '08
 Stites,M

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