The following displays the conditional PMF of a discrete rv X given Y. 0.2, r = -10 0.2, r = -10 PX|r(x|Y = 15) ={0.6, r = 0 , Px|r(x|Y = 5) = { 0.7, x = 0 0.2, * = 10. 0.1, r = 10. Suppose that the marginal PMF of Y is py (15) = 0.3, py (5) = 0.7. (i) Compute the conditional mean E[X|Y = 15]. (ii) Compute the conditional probability P(Y = 15|X = 10). (iii) Let W = max(X,Y). What is the support of W? Obtain the PMF of W.

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The following displays the conditional PMF of a discrete rv X given Y.
0.2, r = -:
: = -10
0.2, r = -10
Px|Y (x|Y = 15) ={0.6, r = 0 , Px|r(x|Y = 5) = { 0.7, x = 0
0.2, x = 10.
0.1,
x = 10.
Suppose that the marginal PMF of Y is py(15) = 0.3, py(5) = 0.7.
(i) Compute the conditional mean E[X|Y = 15].
(ii) Compute the conditional probability P(Y = 15|X = 10).
(iii) Let W = max(X, Y). What is the support of W? Obtain the PMF of W.
Transcribed Image Text:The following displays the conditional PMF of a discrete rv X given Y. 0.2, r = -: : = -10 0.2, r = -10 Px|Y (x|Y = 15) ={0.6, r = 0 , Px|r(x|Y = 5) = { 0.7, x = 0 0.2, x = 10. 0.1, x = 10. Suppose that the marginal PMF of Y is py(15) = 0.3, py(5) = 0.7. (i) Compute the conditional mean E[X|Y = 15]. (ii) Compute the conditional probability P(Y = 15|X = 10). (iii) Let W = max(X, Y). What is the support of W? Obtain the PMF of W.
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