Discrete-time stochastic processes

# Exercise 117 a show that for uncorrelated rvs the

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Unformatted text preview: integral as a sum where X is a non-negative integer random variable. b) Generalize the above integral for the case of an arbitrary (rather than non-negative) rv Y with distribution function FY (y ); use a graphical argument. c) Find E [|Y |] by the same type of argument. d) For what value of α is E [|Y − α|] minimized? Use a graphical argument again. Exercise 1.8. a) Let Y be a nonnegative rv and y &gt; 0 be some ﬁxed number. Let A be the event that Y ≥ y . Show that y IA ≤ Y (i.e., that this inequality is satisﬁed for every ω ∈ ≠). b) Use your result in part a) to prove the Markov inequality. Exercise 1.9. Use the deﬁnition of a limit in the proof of Theorem 1.2 to show that the sequences in parts a and b satisfy limn→1 an = 0 but the sequence in part c does not have a limit. a) an = 1 ln(ln n) b) an = n10 exp(−n) c) an = 1 for n = 10k for each positive integer k and an = 0 otherwise. d) Show that the deﬁnition can be changed (with no change in meaning) by replacing δ with either 1/m or 2−m for every positive integer m. 52 CHAPTER 1. INTRODUCTION AND REVIEW OF PROBABILITY Exercise 1.10. Let X be a rv with distribution function FX (x). Find the distribution function of the following rv’s. a) The maximum of n IID rv’s with distribution function FX (x). b) The minimum of n IID rv’s with distribution FX (x). c) The diﬀerence of the rv’s deﬁned in a) and b); assume X has a density fX (x). Exercise 1.11. a) Let X1 , X2 , . . . , Xn be rv’s with expected values X 1 , . . . , X n . Prove that E [X1 + · · · + Xn ] = X 1 + · · · + X n . Do not assume that the rv’s are independent. b) Now assume that X1 , . . . , Xn are statistically independent and show that the expected value of the product is equal to the product of the expected values. c) Again assuming that X1 , . . . , Xn are statistically independent, show that the variance of the sum is equal to the sum of the variances. Exercise 1.12. Let X1 , X2 , . . . , Xn , . . . be a sequence of IID continuous rv’s with the common probability density function fX (x); note that Pr {X =α} = 0 for all α and that Pr {Xi =Xj } = 0 for all i 6= j . For n ≥ 2, deﬁne Xn as a record-to-date of the sequence if Xn &gt; Xi for all i &lt; n. a) Find the probability that X2 is a record-to-date. Use symmetry to obtain a numerical answer without computation. A one or two line explanation should be adequate). b) Find the probability that Xn is a record-to-date, as a function of n ≥ 1. Again use symmetry. c) Find a simple expression for the expected number of records-to-date that occur over the ﬁrst m trials for any given integer m. Hint: Use indicator functions. Show that this expected number is inﬁnite in the limit m → 1. Exercise 1.13. (Continuation of Exercise 1.12) a) Let N1 be the index of the ﬁrst record-to-date in the sequence. Find Pr {N1 &gt; n} for each n ≥ 2. Hint: There is a far simpler way to do this than working from part b in Exercise 1.12. b) Show that N1 is a rv. c) Show that E [N1 ] = 1. d) Let N2 be the index of the second record-to-date in the sequence. Show that N2 is a rv. Hint: You need not ﬁnd the distribution function of N2 here. e) Contrast your result in part c to the result from part c of Exercise 1.12 saying that the expected number of records-to-date is inﬁnite over an an inﬁnite number of trials. Note: this should be a shock to your intuition — there is an inﬁnite expected wait for the ﬁrst of an inﬁnite sequence of occurrences. 1.8. EXERCISES 53 Exercise 1.14. (Another direction from Exercise 1.12) a) For any given n ≥ 2, ﬁnd the probability that Nn and Xn+1 are both records-to-date. Hint: The idea in part b of 1.12 is helpful here, but the result is not. b) Is the event that Xn is a record-to-date statistically independent of the event that Xn+1 is a record-to-date? c) Find the expected number of adjacent pairs of records-to-date over the sequence X1 , X2 , . . . . 1 1 Hint: A helpful fact here is that n(n1 = n − n+1 . +1) Exercise 1.15. a) Assume that X is a discrete rv taking on values a1 , a2 , . . . , and let Y = g (X ). Let bi = g (ai ), i≥1 be the ith value taken on by Y . Show that E [Y ] = P P i bi pY (bi ) = i g (ai )pX (ai ). b) Let X be a continuous rv with density fXR x) and let g be diﬀerentiable and monotonic ( R increasing. Show that E [Y ] = y fY (y )dy = g (x)fX (x)dx. Exercise 1.16. a) Consider a positive, integer-valued rv whose distribution function is given at integer values by FY (y ) = 1 − 2 (y + 1)(y + 2) for integer y ≥ 0 Use (1.24) to show that E [Y ] = 2. Hint: Note the PMF given in (1.21). b) Find the PMF of Y and use it to check the value of E [Y ]. c) Let X be another positive, integer-valued rv. Assume its conditional PMF is given by pX |Y (x|y ) = 1 y for 1 ≤ x ≤ y Find E [X | Y = y ] and show that E [X ] = 3/2. Explore ﬁnding pX (x) until you are convinced that using the conditional expectation to calculate E [X ] is considerably easier than using pX (x). d) Let Z be another integer-valued rv with the conditional PMF pZ |Y (z |y ) = 1 y2 for 1 ≤ z ≤ y 2 Find E [Z | Y = y ] for each integer y ≥ 1 and ﬁnd E [Z ]. Exercise 1.17...
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