� Massachusetts Institute of Technology Department of Electrical Engineering & Computer Science 6.041/6.431: Probabilistic Systems Analysis (Fall 2010) Problem Set 4 Due October 6, 2010 1. Random variables X and Y have the joint PMF c(x2 + y2), if x ∈ {1, 2, 4} and y ∈ {1, 3}, pX,Y (x, y) = 0, otherwise. (a) What is the value of the constant c? (b) What is P(Y < X)? (c) What is P(Y > X)? (d) What is P(Y = X)? (e) What is P(Y = 3)? (f) Find the marginal PMFs pX (x) and pY (y). (g) Find the expectations E[X], E[Y ] and E[XY ]. (h) Find the variances var(X), var(Y ) and var(X + Y ). (i) Let A denote the event X ≥ Y . Find E[X | A] and var(X | A). 2. The newest invention of the 6.041/6.431 staff is a three-sided die with faces numbered 1, 2, and 3. The PMF for the result of any one roll of this die is pX (x) = ⎧⎪⎪⎪⎨ ⎪⎪⎪⎩ 1/2, if x = 1, 1/4, if x = 2, 1/4, if x = 3, 0, otherwise. Consider a sequence of six independent rolls of this die, and let Xi be the random variable corresponding to the ith roll. (a) What is the probability that exactly three of the rolls have result equal to 3? (b) What is the probability that the first roll is 1, given that exactly two of the six rolls have result of 1? (c) We are told that exactly three of the rolls resulted in 1 and exactly three resulted in 2. Given this information, what is the probability that the sequence of rolls is 121212? (d) Conditioned on the event that at least one roll resulted in 3, find the conditional PMF of the number of 3’s. 3. Suppose that X and Y are independent, identically distributed, geometric random variables with parameter p. Show that 1P(X = i | X + Y = n) = n − 1, for i = 1, 2, . . . , n − 1. Page 1 of 2� Massachusetts Institute of Technology Department of Electrical Engineering & Computer Science 6.041/6.431: Probabilistic Systems Analysis (Fall 2010) 4. Consider 10 independent tosses of a biased coin with a probability of heads of p. (a) Let A be the event that there are 6 heads in the first 8 tosses. Let B be the event that the 9th toss results in heads. Show that events A and B are independent. (b) Find the probability that there are 3 heads in the first 4 tosses and 2 heads in the last 3 tosses. (c) Given that there were 4 heads in the first 7 tosses, find the probability that the 2nd head occurred during the 4th trial. (d) Find the probability that there are 5 heads in the first 8 tosses and 3 heads in the last 5 tosses. 5. Consider a sequence of independent tosses of a biased coin at times t = 0, 1, 2, . . .. On each toss, the probability of a ’head’ is p, and the probability of a ’tail’ is 1 − p. A reward of one unit is given each time that a ’tail’ follows immediately after a ’head.’ Let R be the total reward paid in times 1, 2, . . . , n. Find E[R] and var(R). G1†. A simple example of a random variable is the indicator of an event A, which is denoted by IA: IA(ω) = 1, if ω ∈ A 0, otherwise. (a) Prove that two events A and B are independent if and only if the associated indicator random variables, IA and IB are independent. (b) Show that if X = IA, then E[X] = P(A). †Required for 6.431; optional for 6.041 Page 2 of 2MIT OpenCourseWare http://ocw.mit.edu 6.041 / 6.431 Probabilistic Systems Analysis and Applied Probability Fall 2010 For information about citing these materials or our Terms of Use, visit:
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