Functions of Random Variables -...
Transcript of Functions of Random Variables -...
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Functions of Random VariablesLecture 19-20
Starting November 6
The probability that we may fail in the struggle ought not to deter us from the support of a cause we believe to be just.
Abraham Lincoln
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Outline
• Four Methods for Function of Random Variables
1) Discrete case: calculate directly. 2) Method of m.g.f3) Method of c.d.f4) Method of transforms
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Functions of Random Variable
You know distribution of X and Y. Want to know distribution of function of X and Y like X+Y, X2, eX+Y, g(X,Y)
Many ways to attach the problem1) Discrete case: calculate directly. 2) Method of m.g.f3) Method of c.d.f4) Method of transforms
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Discrete Problems
Given X has pdf,
What is the distribution of U=2XWhat is the distribution of V=(X-3)2
x 1 2 3 4 5
f(x) .1 .3 .3 .2 .1
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Discrete Problems
U=2X has pdf,
U 2 4 6 8 10
f(u) .1 .3 .3 .2 .1
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Discrete Problems
What is the distribution of V=(X-3)2
What is value set of V? {0,1,4}
x 1 2 3 4 5
V 4 1 0 1 4
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Steps
What is Value Set of V
x 1 2 3 4 5
v 4 1 0 1 4
( 0) ( 3) .3( 1) ( 2) ( 4) .3 .2
P V P XP V P X P X
= = = == = = + = = +
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Calculate pdf
v 0 1 4
f(v) .3 .5 .2
( 0) ( 3).3( 1) ( 2) ( 4) .3 .2( 4) ( 1) ( 5) .2
P V P XP V P X P XP V P X P X
= = == = = + = = += = = + = =
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General Steps
a) Identify value sets of X
and value set of V=g(X)
a) Calculate pdf
{1, 2, 3, 4, 5}S =
' {0,1, 4}S =
For each ( ) ( ) ( )
{ | ( ( ) )
V Xx A
f v P V v f x
A x g x v∈
= = =
= =
∑
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Works for X continuous and U discrete as well
Let X be uniform continuous RV on interval [0,20]
Let 1 0 2( ) 15 2 8
20 8 20
xU g x x
x
< ≤⎧⎪= = < ≤⎨⎪ < <⎩
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General Steps
a) Identify value sets of X
and value set of U=g(X)
a) Calculate pdf
(0, 20)S =
' {0,15, 20}S =
2
0
( 1) (0 2) 1/ 20 1/10P U P X dx= = < ≤ = =∫
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Calculate pdf
U 1 15 20
f(u) 1/10 6/20 12/20
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MGF Method
Moment generating function can be used to calculate the distribution of sums of independent random variables.
If are independent random variables with mgf then
has mgf
1 2, , , nX X X…( )
iXm t
1
n
ii
W X=
=∑1
( ) ( )i
m
W Xi
m t m t=
=∏
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ExampleLet X be the number of heads in ten tosses. Let Y be the number of heads in 20 tosses. What is the distribution of X +Y?
X~binomial (n=10,p=0.5)Y~binomial(n=20,p=0.5)The mgf of binomial is So the mgf of X+Y is
( (1 ))t npe p+ −
10 20 30( ) ( ) ( ) (0.5 0.5) (0.5 0.5) (0.5 0.5)t t tX Y X Ym t m t m t e e e+ = = + + = +
Thus X+Y is binomial n=30 p=0.5
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Example
Ten families in a neighborhood have children until they get a girl. What is the distribution of the total number of kids in the ten families?
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ExampleThe number of kids in a family is
Xi~geometric (p=0.5)The mgf of a geometric is
The mgf of sum of 10 geometrics is
This is a negative binomial r=10 p=p!
( )1 (1 )
t
t
pem tp e
=− −
10
( )1 (1 )i
t
tX
pem tp e
⎡ ⎤= ⎢ ⎥∑ − −⎣ ⎦
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Notes on MGF method
MGF is quite valuable for deriving many important results.
Try the ones on the next page for practice.But it only works in limited cases
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Let X and Y be independent
distr X Y X+Y
binomial n1,p n2,p Binomial(n1+n2,p)
Poisson λ1 λ2 Poisson(λ1+λ2 )
Negbinomial
(r1,p) (r2,p) Neg Bin.(r1+r2,p)
Gamma (θ,κ1) (θ,κ2) Gamma(θ,κ1+ κ2))
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X+Y frequently not same type of distribution as X and Y
If Xi, i=1..n are independent and identically distributed geometric(p) then the distribution of the sum of Xi’s is negative binomial (n,p)
If Xi, i=1..n are independent and identically distributed exponential(θ) then the distribution of the sum of Xi’s is gamma (θ,n.)
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Method of the CDF
• Intuitively• Formally
– 1-1 function
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Continuous case
X~uniform(0,2)U=X2
What is value set of U?
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
0
0.5
1
1.5
2
2.5
3
3.5
4
x
x2
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Continuous case
X~uniform(0,2)U=X2
What is value set of U?
What is P(U≤1)? P(U≤1.5)?P(U≤c)?
What is cdf of U?
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Continuous case
X~uniform(0,2)U=X2
What is P(U≤1)?P(U ≤3)?
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
0
0.5
1
1.5
2
2.5
3
3.5
4
x
x2
1
0
1 1( 1) ( 1) (1)2 2X
x
P U P X F dx=
≤ = ≤ = = =∫3
0
1 3( 3) ( 3) ( 3) 0.8862 2X
x
P U P X F dx=
≤ = ≤ = = = =∫
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Continuous case
What is cdf of U?
Recall
What is pdf of U?
( ) '( )Uf u F u=
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Continuous case
What is cdf of U?Calculate F(c)=P(U≤c)
What is pdf of U?
0
0 0
1( ) ( ) ( ) ( ) 0 42 2
1 4
c
U X
c
cF c P U c P X c F c dx c
c
<=⎧⎪⎪= ≤ = ≤ = = = < <⎨⎪
≥⎪⎩
∫
1/ 2 1/ 2( / 2)( ) '( )4U U
d u uf u F udu
−
= = = 1 0 4( ) 4
0 . .U
uf u u
o w
⎧ < <⎪= ⎨⎪⎩
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Method of cdf
Basic idea: If U=g(x), Find cdf of U as a function of cdf of X. Differentiate.
Simplifications possible:
Consider case of 1 to 1 functions.
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1 to 1 increasing functionsIf U is 1 to 1 increasing function
u=g(x) →x=g-1(u)=h(u)Then the cdf of U is given by
And the pdf of U is given byby chain rule
No need to explicitly make cdf!
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
0
0.5
1
1.5
2
2.5
3
3.5
4
x
x2
1( ) ( ( ) ) ( ( )) ( ( ))U XF c P g X u P X g u F h u−= ≤ = ≤ =
( ( )) ( ( ))( ) ( ( ))XU X
dF h u d h uf u f h udu du
= =
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1 to 1 increasing
X~uniform(0,2)U=X2
Inverse
pdf0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
0
0.5
1
1.5
2
2.5
3
3.5
4
x
x2
1
1/ 2
( ) ( )( ( ))
2
g u h u ud h u u
du
−
−
= =
=
1/ 2 1/ 2( ( )) 1( ) ( ( )) 0 42 2 4U X
d h u u uf u f h u udu
− −
= = = < <
u
1( )g u−
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You try
Let Z be a standard normal with mean 0 and std dev. 1.
Find pdf of U=2Z+3, using the method just shown. Is this a 1-1 increasing function?( )( ( ))
( ( ))( ) ( ( ))u Z
h ud h u
dud h uf u f h u
du
=
=
= =
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Decreasing function
Let Z be a standard normal with mean 0 and std dev. 1.
Find pdf of U=-2Z+3, using the method just shown. This is a 1-1 decreasing function!
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1 to 1 decreasing functions
If U is 1 to 1 decreasing functionu=g(x) →x=g-1(u)=h(u)
Then the cdf of U is given by
And the pdf of U is given by
by chain rule
1( ) ( ( ) ) ( ( )) 1 ( ( ))U XF c P g X u P X g u F h u−= ≤ = ≥ = −
(1 ( ( ))) ( ( ))( ) ( ( ))XU
d F h u d h uf u f h udu du
− ⎡ ⎤= = −⎢ ⎥⎣ ⎦
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
1
2
3
4
5
6
7
x
1/x
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In general
Increasing functions
Decreasing functions
So for general 1 to 1 functions, the pdf is( ( )) ( ( ))( ) ( ( ))U
Ud F u d h uf u f h u
du du= =
( ( )) 0d h udu
≥
( ( )) 0d h udu
≤
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Example
Let Find pdf ofValue set U in (1,∞)
2( ) 3 0 1Xf x x x= < <3U X −=
1/3
4/3
1/3 2 4/3 2
( )( ( )) 1
3( ( )) 1( ) ( ( )) 3( ) 1
3u
h u ud h u u
dud h uf u f h u u u u u
du
−
−
− − −
=
=−
= = = <
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
100
200
300
400
500
600
700
800
900
x
x-3
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Method of Joint Transforms
If X is a vector of continuous R.V.
If U=G(X) is 1 to 1 and the inverse function x=g-1(u)=h(u) is differentiable, then the pdfof U is given by
Where |J(u)| is thedeterminant of the Jacobian
1 2( , , , )nx x x…
( ) ( ( )) ( )U Xf u f h u J u=1 1
1 2
2 2
1 2
| |
x xu u
Jx xu u
∂ ∂∂ ∂
=∂ ∂∂ ∂
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Example
Let
Find pdf
( )( , ) 0 0X YXYf x y e X Y− += < < ∞ < < ∞
U X Y= +
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• Step 0 – figure out value set 0<U• Step 1: find 1 to 1 transform
U=X+Y V=Y
In general you can use any function (simple as possible) such that U=a(X,Y), V=b(X,Y) is 1 to 1
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• Step 2 – Solve for (x,y) in terms of (u,v)U=X+Y V=Y
Y=V X=U-V• Step 3 Calculate the range of U and V0<X<∞ → 0<U-V<∞0<Y<∞→ 0<V<∞
0<V<U< ∞
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• Step 4 – Calculate Jacobian of Y=V X=U-V
1 1| | | (1)(1) (0)( 1) | |1|
0 1
xxu v
Jy yu v
∂∂∂ ∂ −
= = = − − =∂ ∂∂ ∂
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Step 5 and 6
• Use formula to calculate the joint of U and V
• Calculate marginal of U
( ), ( , ) ( , ) | | (1) 0u v v
U V Xf u v f u v v J e v u− − += − = < < < ∞
( )
0
( ) 0U
u uUf u e dv ue u− −= = < < ∞∫
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Problem
The joint density of X1 and X2 are given by
Find a) The joint density of Y=X1+X2 and Z=X1.b) The marginal density of Y
1 21 2
1 0 1,0 1( , )
0x x
f x xotherwise
< < < <⎧= ⎨⎩
![Page 41: Functions of Random Variables - homepages.rpi.eduhomepages.rpi.edu/~bennek/class/probold/handouts/Lecture_19-08.pdfFunctions of Random Variable You know distribution of X and Y.Want](https://reader033.fdocuments.net/reader033/viewer/2022050120/5f50aa6006b803190653ad4d/html5/thumbnails/41.jpg)
Solution
Solving for y and z
The Jacobian is
The new range is
1 2 2
1 2
, , we get, .
y x x z xx y z x z= + == − =
1 11
0 1J
−= =
1 0 1z y z and z< < + < <
![Page 42: Functions of Random Variables - homepages.rpi.eduhomepages.rpi.edu/~bennek/class/probold/handouts/Lecture_19-08.pdfFunctions of Random Variable You know distribution of X and Y.Want](https://reader033.fdocuments.net/reader033/viewer/2022050120/5f50aa6006b803190653ad4d/html5/thumbnails/42.jpg)
continued
The joint pdf of y and z isf(y,z)=1*|1| = 1
To get pdf of z. Integrate out y
1 0 1z y z and z< < + < <
0
1
1
1 0 10 0
1 0 1( )
1 2 1 2
0 2
y
y
z y z a n d zy
d z y yf y
d z y y
f o r y−
< < + < <
≤⎧⎪⎪ = < <⎪⎪= ⎨⎪ = − < <⎪⎪⎪ ≥⎩
∫
∫