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Unformatted text preview: EE 278 November 11, 2009 Statistical Signal Processing Handout #17 Homework #7 Due: Wednesday, November 18 1. Convergence examples. Consider the following sequences of random variables defined on the probability space (Ω , F , P), where Ω = { , 1 ,...,m 1 } , F is the collection of all subsets of Ω, and P is the uniform distribution over Ω. X n ( ω ) = braceleftBigg 1 n ω = n mod m otherwise Y n ( ω ) = braceleftBigg 2 n ω = 1 otherwise Z n ( ω ) = braceleftBigg 1 ω = 1 otherwise Which of these sequences converges to zero (a) with probability one, (b) in mean square, and/or (c) in probability? Justify your answers. 2. Convergence with probability 1. Let X 1 ,X 2 ,X 3 ,... be i.i.d. random variables with X i ∼ Exp( λ ). Show that the sequence of random variables Y n = min { X 1 ,...,X n } converges with probabil ity 1. What is the limit? 3. Convergence in probability. (Bonus) Let X 1 ,X 2 ,X 3 ,... be a sequence of nonnegative random variables such that lim n →∞ E( X n ) = 0. a. Does the sequence X n converge in probability? If so, what is the limit? Justify your answer mathematically. b. Does the sequence Y n = 1 e X n converge in probability? If so, what is the limit? Justify your answer mathematically. 4. Vector CLT. The signal received over a wireless communication channel can be represented by two sums X 1 n = 1 √ n n summationdisplay j =1 Z j cos Θ j and X 2 n = 1 √ n n summationdisplay j =1 Z j sin Θ...
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This note was uploaded on 11/28/2009 for the course EE 278 taught by Professor Balajiprabhakar during the Fall '09 term at Stanford.
 Fall '09
 BalajiPrabhakar
 Signal Processing

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