9. two functions of two random variablesee162.caltech.edu/notes/lect9.pdftwo functions of two random...
TRANSCRIPT
![Page 1: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/1.jpg)
1
9. Two Functions of Two Random Variables
In the spirit of the previous lecture, let us look at an immediate generalization: Suppose X and Y are two random variables with joint p.d.f Given two functions and define the new random variables
How does one determine their joint p.d.f Obviouslywith in hand, the marginal p.d.fs and can be easily determined.
(9-1)
).,( yxfXY
).,(
),(
YXhW
YXgZ
==
),( yxg
),,( yxh
(9-2)
?),( wzfZW
),( wzfZW )(zfZ )(wfW
![Page 2: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/2.jpg)
2
The procedure is the same as that in (8-3). In fact for given zand w,
where is the region in the xy plane such that the inequalities and are simultaneously satisfied.
We illustrate this technique in the next example.
( ) ( )( ) ∫ ∫ ∈
=∈=
≤≤=≤≤=
wzDyx XYwz
ZW
dxdyyxfDYXP
wYXhzYXgPwWzZPwzF
,),( , ,),(),(
),(,),()(,)(),( ξξ
(9-3)
wzD ,
zyxg ≤),( wyxh ≤),(
x
y
wzD ,
Fig. 9.1
wzD ,
![Page 3: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/3.jpg)
3
Example 9.1: Suppose X and Y are independent uniformly distributed random variables in the interval Define DetermineSolution: Obviously both w and z vary in the interval Thus
We must consider two cases: and since theygive rise to different regions for (see Figs. 9.2 (a)-(b)).
).,max( ),,min( YXWYXZ ==
.0or 0 if ,0),( <<= wzwzFZW
).,( wzf ZW
).,0( θ
).,0( θ(9-4)
( ) ( ). ),max( ,),min(,),( wYXzYXPwWzZPwzFZW ≤≤=≤≤= (9-5)
zw ≥ ,zw <
wzD ,
X
Y
wy =
),( ww
),( zz
zwa ≥ )(
X
Y
),( ww
),( zz
zwb < )(
Fig. 9.2
![Page 4: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/4.jpg)
4
For from Fig. 9.2 (a), the region is represented by the doubly shaded area. Thus
and for from Fig. 9.2 (b), we obtain
With
we obtain
Thus
, , ),(),(),(),( zwzzFzwFwzFwzF XYXYXYZW ≥−+=
wzD ,
(9-6)
,zw ≥
,zw <. , ),(),( zwwwFwzF XYZW <= (9-7)
,)( )(),(2θθθ
xyyxyFxFyxF YXXY =⋅== (9-8)
2
2 2
(2 ) / , 0 ,( , )
/ , 0 .ZW
w z z z wF z w
w w z
θ θθ θ
⎧ − < < <= ⎨ < < <⎩
⎩⎨⎧ <<<
=.otherwise,0
,0,/2),(
2 θθ wzwzf ZW
(9-9)
(9-10)
![Page 5: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/5.jpg)
5
From (9-10), we also obtain
and
If and are continuous and differentiable functions, then as in the case of one random variable (see (5-30)) it is possible to develop a formula to obtain the joint p.d.f directly. Towards this, consider the equations
For a given point (z,w), equation (9-13) can have many solutions. Let us say
,0 ,12
),()(
θ
θθθ
<<⎟⎠⎞
⎜⎝⎛ −== ∫ z
zdwwzfzf
z ZWZ (9-11)
.),( ,),( wyxhzyxg == (9-13)
.0 ,2
),()(
0 2 θθ
<<== ∫ ww
dzwzfwfw
ZWW(9-12)
),( yxg ),( yxh
),( wzfZW
),,( , ),,( ),,( 2211 nn yxyxyx
![Page 6: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/6.jpg)
6
represent these multiple solutions such that (see Fig. 9.3)
(9-14).),( ,),( wyxhzyxg iiii ==
Fig. 9.3
Consider the problem of evaluating the probability
z
(a)
w
•),( wz
z∆
w∆ww ∆+
zz ∆+
( )( ). ),(,),(
,
wwYXhwzzYXgzP
wwWwzzZzP
∆+≤<∆+≤<=∆+≤<∆+≤<
(9-15)
(b)
x
y1∆ 2∆
i∆
n∆
•
•
•
•
),( 11 yx
),( 22 yx
),( ii yx
),( nn yx
![Page 7: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/7.jpg)
7
Using (7-9) we can rewrite (9-15) as
But to translate this probability in terms of we need to evaluate the equivalent region for in the xy plane. Towards this referring to Fig. 9.4, we observe that the point A with coordinates (z,w) gets mapped onto the point with coordinates (as well as to other points as in Fig. 9.3(b)). As z changes to to point B in Fig. 9.4 (a), let represent its image in the xy plane. Similarly as w changes to to C, let represent its image in the xy plane.
(9-16)
),,( yxf XY
( ) .),(, wzwzfwwWwzzZzP ZW ∆∆=∆+≤<∆+≤<
wz ∆∆
A′),( ii yx
zz ∆+ B′
ww ∆+ C′
(a)
w
Az∆
w∆w
B
zz
C D
Fig. 9.4 (b)
y
A′iy B′
xix
C′D′
i∆θ ϕ
![Page 8: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/8.jpg)
8
Finally D goes to and represents the equivalent parallelogram in the XY plane with area Referring back to Fig. 9.3, the probability in (9-16) can be alternatively expressed as
Equating (9-16) and (9-17) we obtain
To simplify (9-18), we need to evaluate the area of the parallelograms in Fig. 9.3 (b) in terms of Towards this, let and denote the inverse transformation in (9-14), so that
DCBA ′′′′.i∆
,D′
( ) .),(),( ∑∑ ∆=∆∈i
iiiXYi
i yxfYXP (9-17)
.),(),( ∑ ∆∆∆=
i
iiiXYZW wz
yxfwzf (9-18)
.wz∆∆i∆
1g 1h
).,( ),,( 11 wzhywzgx ii == (9-19)
![Page 9: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/9.jpg)
9
As the point (z,w) goes to the point the point and the point Hence the respective x and y coordinates of are given by
and
Similarly those of are given by
The area of the parallelogram in Fig. 9.4 (b) isgiven by
,),( Ayx ii ′≡ ,),( Bwzz ′→∆+,),( Cwwz ′→∆+ .),( Dwwzz ′→∆+∆+
B′
,),(),( 1111 z
z
gxz
z
gwzgwzzg i ∆
∂∂+=∆
∂∂+=∆+
.),(),( 1111 z
z
hyz
z
hwzhwzzh i ∆
∂∂+=∆
∂∂+=∆+
(9-20)
(9-21)
C′
. , 11 ww
hyw
w
gx ii ∆
∂∂+∆
∂∂+ (9-22)
DCBA ′′′′
( ) ( )( ) ( ) ( ) ( ). cos sinsin cos
)sin(
θϕθϕϕθ
CABACABA
CABAi
′′′′−′′′′=−′′′′=∆
(9-23)
![Page 10: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/10.jpg)
10
But from Fig. 9.4 (b), and (9-20) - (9-22)
so that
and
The right side of (9-27) represents the Jacobian of the transformation in (9-19). Thus
.cos ,sin
,sin ,cos
11
11
ww
gCAz
z
hBA
ww
hCAz
z
gBA
∆∂∂=′′∆
∂∂=′′
∆∂∂=′′∆
∂∂=′′
θϕ
θϕ
(9-25)
(9-24)
wzz
h
w
g
w
h
z
gi ∆∆⎟
⎠⎞
⎜⎝⎛
∂∂
∂∂−
∂∂
∂∂=∆ 1111 (9-26)
⎟⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜⎜
⎝
⎛
∂∂
∂∂
∂∂
∂∂
=⎟⎠⎞
⎜⎝⎛
∂∂
∂∂−
∂∂
∂∂=
∆∆∆
w
h
z
h
w
g
z
g
z
h
w
g
w
h
z
g
wzi
11
11
1111 det (9-27)
( , )J z w
![Page 11: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/11.jpg)
11
1 1
1 1
( , ) d e t .
g g
z wJ z w
h h
z w
∂ ∂⎛ ⎞⎜ ⎟∂ ∂⎜ ⎟
= ⎜ ⎟⎜ ⎟∂ ∂⎜ ⎟
∂ ∂⎝ ⎠
(9-28)
Substituting (9-27) - (9-28) into (9-18), we get
since
where represents the Jacobian of the original transformation in (9-13) given by
,),(|),(|
1),(|),(|),( ∑∑ ==
iiiXY
iiiiiXYZW yxf
yxJyxfwzJwzf (9-29)
|),(|
1 |),(|
ii yxJwzJ = (9-30)
( , )i iJ x y
.det),(
, ii yyxx
ii
y
h
x
h
y
g
x
g
yxJ
==⎟⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜⎜
⎝
⎛
∂∂
∂∂
∂∂
∂∂
= (9-31)
![Page 12: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/12.jpg)
12
Next we shall illustrate the usefulness of the formula in (9-29) through various examples:
Example 9.2: Suppose X and Y are zero mean independent Gaussian r.vs with common variance Define where Obtain Solution: Here
Since
if is a solution pair so is From (9-33)
),/(tan , 122 XYWYXZ −=+=
.2σ
).,( wzfZW
.2
1),(
222 2/)(2
σ
πσyx
XY eyxf +−= (9-32)
,2/||),/(tan),(;),( 122 π≤==+== − wxyyxhwyxyxgz (9-33)
),( 11 yx ).,( 11 yx −−
.tanor ,tan wxywx
y == (9-34)
.2/|| π≤w
![Page 13: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/13.jpg)
13
Substituting this into z, we get
and
Thus there are two solution sets
We can use (9-35) - (9-37) to obtain From (9-28)
so that
.cos or ,sec tan1 222 wzxwxwxyxz ==+=+= (9-35)
.sintan wzwxy == (9-36)
.sin ,cos ,sin ,cos 2211 wzywzxwzywzx −=−=== (9-37)
).,( wzJ
,cossin
sincos),( z
wzw
wzw
w
y
z
y
w
x
z
x
wzJ =−
=
∂∂
∂∂
∂∂
∂∂
= (9-38)
.|),(| zwzJ = (9-39)
![Page 14: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/14.jpg)
14
We can also compute using (9-31). From (9-33),
Notice that agreeing with (9-30). Substituting (9-37) and (9-39) or (9-40) into (9-29), we get
Thus
which represents a Rayleigh r.v with parameter and
),( yxJ
.11
),(22
2222
2222
zyx
yx
x
yx
y
yx
y
yx
x
yxJ =+
=
++−
++= (9-40)
|,),(|/1|),(| ii yxJwzJ =
( )
.2
|| ,0 ,
),(),(),(
22 2/2
2211
ππσ
σ <∞<<=
+=
− wzez
yxfyxfzwzf
z
XYXYZW
(9-41)
,0 ,),()(22 2/
2
2/
2/ ∞<<== −
−∫ zez
dwwzfzf zZWZ
σπ
π σ(9-42)
,2σ
,2
|| ,1
),()(
0
ππ
<== ∫∞
wdzwzfwf ZWW (9-43)
![Page 15: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/15.jpg)
15
which represents a uniform r.v in the interval Moreover by direct computation
implying that Z and W are independent. We summarize these results in the following statement: If X and Y are zero mean independent Gaussian random variables with common variance, then has a Rayleigh distribution and has a uniform distribution. Moreover these two derived r.vsare statistically independent. Alternatively, with X and Y as independent zero mean r.vs as in (9-32), X + jY represents a complex Gaussian r.v. But
where Z and W are as in (9-33), except that for (9-45) to hold good on the entire complex plane we must have and hence it follows that the magnitude and phase of
).2/,2/( ππ−
22 YX + )/(tan1 XY−
,jWZejYX =+ (9-45)
)()(),( wfzfwzf WZZW ⋅= (9-44)
,ππ <<− W
![Page 16: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/16.jpg)
16
a complex Gaussian r.v are independent with Rayleighand uniform distributions ~ respectively. The statistical independence of these derived r.vs is an interesting observation.
Example 9.3: Let X and Y be independent exponential random variables with common parameter λ. Define U = X + Y, V = X - Y. Find the joint and marginal p.d.f of U and V. Solution: It is given that
Now since u = x + y, v = x - y, always and there is only one solution given by
Moreover the Jacobian of the transformation is given by
.0 ,0 ,1
),( /)(2
>>= +− yxeyxf yxXY
λ
λ(9-46)
.2
,2
vuy
vux
−=+= (9-47)
,|| uv <
( )),( ππ−U
![Page 17: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/17.jpg)
17
and hence
represents the joint p.d.f of U and V. This gives
and
Notice that in this case the r.vs U and V are not independent.
As we show below, the general transformation formula in (9-29) making use of two functions can be made useful even when only one function is specified.
2 11
1 1 ),( −=
−=yxJ
, || 0 ,2
1),( /
2∞<<<= − uvevuf u
UVλ
λ(9-48)
,0 ,2
1),()( /
2
/2
∞<<=== −
−
−
− ∫∫ ueu
dvedvvufuf uu
u
uu
u UVUλλ
λλ(9-49)
/ | |/2 | | | |
1 1( ) ( , ) , .
2 2u v
V UVv vf v f u v du e du e vλ λ
λ λ∞ ∞ − −= = = − ∞ < < ∞∫ ∫ (9-50)
![Page 18: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/18.jpg)
18
Auxiliary Variables:
Suppose
where X and Y are two random variables. To determine by making use of the above formulation in (9-29), we can define an auxiliary variable
and the p.d.f of Z can be obtained from by proper integration.
Example 9.4: Suppose Z = X + Y and let W = Y so that the transformation is one-to-one and the solution is given by
),,( YXgZ =
YWXW == or
(9-51)
(9-52)
),( wzfZW
. , 11 wzxwy −==
)(zfZ
![Page 19: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/19.jpg)
19
The Jacobian of the transformation is given by
and hence
or
which agrees with (8.7). Note that (9-53) reduces to the convolution of and if X and Y are independent random variables. Next, we consider a less trivial example.
Example 9.5: Let ∼ and ∼ be independent. Define
1 1 0
1 1 ),( ==yxJ
),(),(),( 11 wwzfyxfyxf XYXYZW −==
∫∫∞+
∞−−==
,),(),()( dwwwzfdwwzfzf XYZWZ
(9-53)
)(zf X )(zfY
)1,0( UX )1,0( UY
( ) ).2cos(ln2 2/1 YXZ π−= (9-54)
![Page 20: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/20.jpg)
20
Find the density function of Z. Solution: We can make use of the auxiliary variable W = Yin this case. This gives the only solution to be
and using (9-28)
Substituting (9-55) - (9-57) into (9-29), we obtain
( )
,
,
1
2/)2sec(1
2
wy
ex wz
== − π (9-55)
(9-56)
( )
( ) .)2(sec
10
)2(sec
),(
2/)2sec(2
12/)2sec(2
11
11
2
2
wz
wz
ewz
w
xewz
w
y
z
y
w
x
z
x
wzJ
π
π
π
π
−
−
−=
∂∂−
=
∂∂
∂∂
∂∂
∂∂
= (9-57)
( ) 2sec( 2 ) / 22( , ) sec (2 ) ,
, 0 1,
z wZWf z w z w e
z w
ππ −=− ∞ < < +∞ < <
(9-58)
![Page 21: 9. Two Functions of Two Random Variablesee162.caltech.edu/notes/lect9.pdfTwo Functions of Two Random Variables In the spirit of the previous lecture, let us look at an immediate generalization:](https://reader031.vdocuments.site/reader031/viewer/2022020303/5ae551077f8b9acc268be03c/html5/thumbnails/21.jpg)
21
and
Let so that Notice that as w varies from 0 to 1, u varies from to Using this in (9-59), we get
which represents a zero mean Gaussian r.v with unit variance. Thus ∼ Equation (9-54) can be used as a practical procedure to generate Gaussian random variables from two independent uniformly distributed random sequences.
( ) 22 1 1 tan(2 ) / 2/ 2 2
0 0( ) ( , ) sec (2 ) .z wz
Z ZWf z f z w dw e z w e dwππ −−= =∫ ∫tan(2 )u z wπ= 22 sec (2 ) .du z w dwπ π=
∞− .∞+
, ,2
1
2
2
1)( 2/
1
2/2/ 222
∞<<∞−== −∞+
∞−
−− ∫ zedu
eezf zuzZ πππ
(9-59)
(9-60)
).1,0( NZ