Solutions to Probability Review | Random Processes | ECE 534, Study notes of Electrical and Electronics Engineering

Material Type: Notes; Class: Random Processes; Subject: Electrical and Computer Engr; University: University of Illinois - Urbana-Champaign; Term: Spring 2005;

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Pre 2010

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University of Illinois at Urbana-Champaign
Department of Electrical and Computer Engineering
ECE 434: Random Processes
Spring 2005
Solutions to Probability Review
Problem 1.1
(a) True. If X, Y are conditionally independent on Z, then fX Y |Z(x, y|z) = fX|Z(x|z)·
fY|Z(y|z). Here fXY |Z(x, y|z) = 4
fZ(z)ezx ·ezy ,Xand Ycomponents are uncoupled,
thus they are conditionally independent.
(b) True.
V ar(X+Y) = E[(x+ymxmy)2] = E[((xmx)+(ymy))2]
=E[(xmx)2] + E[(ymy)2] = V ar(X) + V ar(Y).
(c) False.P(AB) = P(A)+ P(B)P(AB) = P(A)+P(B)P(A)P(B)6=P(A) +P(B).
(d) True.P(AB) = P(A) + P(B)P(A)P(B) = P(A) + P(A)P2(A) = 3
2P(A).
P(A) = 1
2.
(e) False.P(Y= 0) = P[X0] = 1
2, so this is not a continuous random variable.
Problem 1.2
(a)
P(Y= 1) = P(Y= 1, X = 0) + P(Y= 1, X = 1)
=P(Y= 1|X= 0)P(X= 0) + P(Y= 1|X= 1)P(X= 1) = p2·1
2+ (1 p1)·1
2
=1 + p2p1
2
(b)
P(X= 0|Y= 1) = P(X= 0, Y = 1)
P(Y= 1) =p2·1
2
1+p2p1
2
=p2
1p1+p2
.
(c) We are asked to find the Bayes rule. Math aside, we can reason this out. The probability
of making a mistake during the transmission, p1and p2, are both less than 1/2. That
means more than half of the time, what we receive at Yis what was sent at source X.
pf3

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University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ECE 434: Random Processes Spring 2005 Solutions to Probability Review

Problem 1.

(a) True. If X, Y are conditionally independent on Z, then fXY |Z (x, y|z) = fX|Z (x|z) · fY |Z (y|z). Here fXY |Z (x, y|z) = (^) fZ^4 (z) e−zx^ · e−zy, X and Y components are uncoupled, thus they are conditionally independent. (b) True.

V ar(X + Y ) = E[(x + y − mx − my)^2 ] = E[((x − mx) + (y − my))^2 ] = E[(x − mx)^2 ] + E[(y − my)^2 ] = V ar(X) + V ar(Y ).

(c) False. P (A∪B) = P (A)+P (B)−P (A∩B) = P (A)+P (B)−P (A)P (B) 6 = P (A)+P (B).

(d) True. P (A ∪ B) = P (A) + P (B) − P (A)P (B) = P (A) + P (A) − P 2 (A) = 32 P (A). P (A) = 12.

(e) False. P (Y = 0) = P [X ≤ 0] = 12 , so this is not a continuous random variable.

Problem 1.

(a)

P (Y = 1) = P (Y = 1, X = 0) + P (Y = 1, X = 1) = P (Y = 1|X = 0)P (X = 0) + P (Y = 1|X = 1)P (X = 1) = p 2 · 1 2

  • (1 − p 1 ) · 1 2 = 1 +^ p^2 −^ p^1 2

(b) P (X = 0|Y = 1) = P^ (X^ = 0, Y^ = 1) P (Y = 1)

p 2 · (^12) 1+p 2 −p 1 2

= p^2 1 − p 1 + p 2

(c) We are asked to find the Bayes rule. Math aside, we can reason this out. The probability of making a mistake during the transmission, p 1 and p 2 , are both less than 1/2. That means more than half of the time, what we receive at Y is what was sent at source X.

When Y = 1:

Xˆ = arg max i=0, 1

P (X = i|Y = 1) = arg max i=0, 1

{P (X = 0|Y = 1), P (X = 1|Y = 1)}

= arg max i=0, 1 {

p 2 1 − p 1 + p 2 ,^

1 − p 1 1 − p 1 + P 2 }

Since p 2 < 12 , 1 − p 1 > 12 , so Xˆ = 1. Similarly we can compute Xˆ when Y = 0. The Bayes decision rule is:

Xˆ =

1 when Y = 1 0 when Y = 0

P (E) = P (X = 0, Y = 1) + P (X = 1, Y = 0) = p^1 + 2 p^2.

Problem 1.

(a) We need fX (x) to integrate to 1:

1 =

b

αe−xdx = α · e−b

α = eb

(b)

E[X] = eb

b

xe−xdx = b + 1

(c) 1. E[X + Y ] = E[X] + E[Y ] = b + 1 + μ.

  1. We don’t have fX,Y (x, y), thus we can’t calculate E[XY ].
  2. We don’t have fX|Y (x|Y = 1), so we can’t calculate this.

Problem 1.

(a) fX,Y (x, y) is constant over the shaded region, thus fX,Y (x, y) = 2, for 0 ≤ x ≤ y ≤ 1.

fX (x) =

fX,Y (x, y)dy =

x

2 dy = 2(1 − x), x ∈ [0, 1]

fY (y) =

fX,Y (x, y)dx =

∫ (^) y

0

2 dx = 2y, y ∈ [0, 1]

(b) No. fX,Y (x, y) 6 = fX (x) · fY (y).

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