Probability Distributions: FXY Function and Independence of Random Variables, Exercises of Probability and Statistics

The fxy function and its relationship to the independence of random variables. Topics include the definition of the fxy function, its properties, and how it relates to the independence of x and y. The document also covers the concept of probability mass functions and the role they play in determining independence.

Typology: Exercises

2011/2012

Uploaded on 08/03/2012

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4.9 a) For x > 0, y > 0
[:1: [Y 2/2
FXY(x, y) = Jo Jo axe-a:1: 2bye-by /2dxdy
= (1 - e-ax2/2)(1 - e-by2/2)
b) P[X>y] = l°O1xaxe-ax2/2bye-by2/2dydX
[00 2 2
= Jo axe-a:1: /2(1 - e-b:1: /2)dx
a
= 1-- a+b
c) Fx(x) = lim Fx(x,y) = 1- e-a:1:2/2 x> 0
y-+oo
* fx(x) = 1;Fx(x) = axe-ax2/2 x > 0
Similarly fy (y) = bye-by2 /2
100 (-1 )00 fx(x) = xe-xe-xYdy = xe-:1: -e-XY = e-:1:
4.13 0 x 0
f( ) - 100 -:1:(l+Y) d- e-x(l+y) ((1 + y)x - 1)100 y y - xe x -
0 (1 + y)2 0
1
-
- (1+y)2
4.17 a) P[X = i,Y ~ y] = P[Y ~ ylX = i]P[X = i]
If X = 1:
1 1 [y-l a
P[Y ~ ylX = l]P[X = 1] = P[N + 1 ~ y]"2 = "2 J-oo 2e-a/zldz
{ !ea(y-l) y < 1
= 1(2 - e-a(y-l)) y > 1
X = -1 is obtained in similar fashion:
{!ea(y+l) y < -1
P[Y ~ ylX = l]P[X = -1] = 1(2 - e-a(y+l)) y ~ -1
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Download Probability Distributions: FXY Function and Independence of Random Variables and more Exercises Probability and Statistics in PDF only on Docsity!

--

4.9 a) For x > 0, y > 0

[:1: [Y 2 / 2 FXY(x, y) = Jo Jo axe-a:1:2bye-by/2dxdy

= (1 - e-ax2/2)(1- e-by2/2)

b) P[X>y] = l°O1xaxe-ax2/2bye-by2/2dydX

[00 2 2 = Jo axe-a:1:/2(1 - e-b:1:/2)dx

a = 1--

a+b

c) Fx(x) = lim Fx(x,y) = 1- e-a:1:2/2 x> 0

y-+oo

  • fx(x) = 1;Fx(x) = axe-ax2/2 x > 0

Similarly

fy (y) = bye-by2/

100 (-

fx(x) = xe-xe-xYdy = xe-:1: -e-XY ) 00 = e-:1:

4.13 0 x 0

f ( ) - 100 -:1:(l+Y) d - e-x(l+y)((1 + y)x - 1)

y y - xe x - 1 00

0 (1 + y)2 0

    • (1+y)

4.17 a) P[X = i,Y ~ y] = P[Y ~ ylX = i]P[X = i]

If X = 1:

1 1 [y-l a

P[Y ~ ylX = l]P[X = 1] = P[N + 1 ~ y]"2= "2J-oo 2e-a/zldz

{ !ea(y-l) y < 1 = 1(2 - e-a(y-l)) y > 1

X = -1 is obtained in similar fashion:

{ !ea(y+l) y < - P[Y ~ ylX = l]P[X = -1] = 1(2 - e-a(y+l)) y ~ -

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b) Fy(y) = pry ~ ylX =]P[X = 1]+ pry ~ ylX = -1]P[X = -1]

= P[N + 1 ~ y]2 + P[N - 1 ~ y]

d Id Id fy(y) = dYFy(y) = 2dYFN(Y- 1) + 2dYFN(Y+ 1)

= 2fN(y - 1) + 2fN(y + 1)

~_. ///II~~~-

I ;..y

0

///'/""""I :~---//'~"'I I ~

-1 0 1 Y

c) P[X = 11Y > 0] = pry > OIX = I]P[X = 1] = 1 - ~e-a

pry > 0] 2P[Y > 0]

P[X = -IIY > 0] = pry > OIX = -1]P[X = -1] = !(1 + !e-a)

pry > 0] 2P[Y > 0]

1(1 -a)

P[X = 11Y> 0] - P[X = -11Y > 0] = 2 - e > 0

2P[Y > 0]

~ X = 1 is more likely

4.24 a) P[X2 < !, IY - 11< !] = P[X2 < !]P[IY - 11 < !]

= P[X < 1 ]P[Y > 1]^ =^1

~ 2 2~

b) P[Xj2 < I,Y > 0] = P[X < 2]P[Y > 0] = 1.

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b) If p = 0 then

x

P[XY > 0] = P[X and Y have same sign]

= JJ ++ fxy(x,y)dxdy+JJ -- fXy(x,y)dxdy

quadrant quadrant

.,~~ =^100 fx(x)dx^100 fy(y)dy^ +^10 fx(x)dx^10 fy(y)dy

~ 0 0 -00 -

i:

;but

100 1 100 -tC/

e-(x-mlr/20"~dx= ~dt = Q (-~ )

0 V2;0"1 -~ V2; 0"

1 and similarly for other integrals, thus

\ P[XY > 0] = Q (-~) Q (-~) + (1 - Q (-~)) (1- Q (-~))

.;

, ,,-~,;,

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