2.1 Random Variable Concept Given an experiment defined by a sample space S with elements s, we...
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Transcript of 2.1 Random Variable Concept Given an experiment defined by a sample space S with elements s, we...
2.1 Random Variable Concept
• Given an experiment defined by a sample space S with elements s, we assign a real number to every s according to some rule as follows;
– X : a function that maps all elements of the sample space into points on the real line
)(sX
Example 2.1-1
• Rolling a die and flipping a coin– Sample space {H,T} x {1,2, ..., 6}– Let the r.v. be a function X as follows
1. A coin head (H) outcome -> positive value shown up on the die
2. A coin tail (T) outcome -> negative and twice value shown up on the die
2.1 Random Variable Concept
Example 2.1-2
• The pointer on a wheel of chance is spun– The possible outcomes
are the numbers from 0 to 12 marked on the wheel
– Sample space {0 < s 12}
– Define a r.v. by the function
– Points in S map onto the real line as the set {0 < x 144}
2)( ssXX
2.1 Random Variable Concept
• Conditions for a Function to be a Random Variable– Every point in S must correspond to only one value of the
r.v.– The set { X x } shall be an event for any real number x
• The set {X(s) x} corresponds to those points s in the sample space for which the X(s) does not exceed the number x
This means that the set is not a set of numbers but a set of experimental outcomes
– The probabilities of the events {X=} and {X=- } shall be 0:• This condition does not prevent X from being either - or for
some values of s; it only requires that the prob. of the set of those s be zero
- -2 -1 0 1 2
... ...The function that maps outcomes in the following manner can not be a r.v.
2.1 Random Variable Concept
• Discrete and Continuous Random Variables– Discrete r.v.:Having only discrete values
• Example 2.1-1 : discrete r.v. defined on a discrete sample space
– Continuous r.v. : Having a continuous range of values• Example 2.1-2 : continuous r.v. defined on a continuous sample
space
– Mixed r.v. : Some of its values are discrete and some are continuous
2.2 Distribution Function
• Cumulative Probability Distribution Function – CDF– The probability of the event {X x} :– Depend on x– A function of x– Distribution function of X– Properties
)()()6(
)()(}{)5(
)()()4(
1)(0)3(
1)()2(
0)()1(
1221
2121
xFxF
xFxFxXxP
xxifxFxF
xF
F
F
XX
XX
XX
X
X
X
}{)( xXPxFX
; consider a discrete distribution
2.2 Distribution Function
• Discrete Random Variable– If X is a discrete r.v., : stair-step form– Amplitude of a step : probability of occurrence of the value of X whe
re the step occurs– If the values of X are denoted xi, we may write
where u(·) is the unit-step function
– Using
)(xFX
N
iiiX xxuxXPxF
1
)(}{)(
00
01)(
x
xxu
}{)( ii xXPxP
N
iii
N
iiiX xxuxPxxuxXPxF
11
)()()(}{)(
2.2 Distribution Function
Fx(3)=Fx(3+)=1 by the property (6)
Example 2.2-1
• Let X have the discrete values in the set {-1, -0.5, 0.7, 1.5, 3}
• The corresponding probabilities {0.1, 0.2, 0.1, 0.4, 0.2}
)()(
ii xx
dx
xxdu
Example 2.2-2
• Wheel-of-chance experiment
2.3 Density Function
• Probability Density Function (PDF) of r.v. X
• Existence– If the derivative of FX(x) exists, then fX(x) exists– If there may be places where dFX(x)/dx is not defined (abrupt
change in slope), FX(x) is a function with step-type discontinuities such as in ex. 2.2-2.
dx
xdFxf X
X)(
)(
2.3 Density Function
– Discrete r.v.• Stair-step form distribution function• Description of the derivative of FX(x) at stairstep points
– Unit-impulse function, (t), used
N
iiiX
N
iiiX
xxxPxf
xxuxPxF
1
1
)()()(
)()()(
2.3 Density Function
• Unit-Impulse Function (t)– Definition by its integral property
(x) : any continuous function at the point x = x0
(t) – Can be integrated as a “function” with infinite amplitude,
area of unity, and zero duration
• The relationship of unit-impulse and unit-step functions
• The general impulse function– Shown symbolically as a vertical arrow occurring at the
point x=x0 and having an amplitude equal to the amplitude of the step function for which it is the derivative
dxxxxx )()()( 00
)()()(
)( xudordx
xdux
x
2.3 Density Function
• A discrete r.v.– The density function for a di
screte r.v. exists
– Using impulse functions to d
escribe the derivative of FX
(x) at its stair step points
N
iiiX
N
iiiX
xxxPxf
xxuxPxF
1
1
)()()(
)()()(
2.3 Density Function
• Properties of Density Functions
2
1
)(}{)4(
)()()3(
1)()2(
)(0)1(
21x
x X
xXX
X
X
dxxfxXxP
dfxF
dxxf
xallxf
Example 2.3-1
• Test the function gX(x) if it can be a valid density function
– Property 1 : nonnegative
– Property 2: Its area a=1 a=1/
Example 2.3-3
• Find its density function
0for 2
1)(
)(
22
xe
b
xe
dx
d
dx
xdFxf b
x
b
xX
X
01)()(
2
bwhereexuxF b
x
X
)(2
)(
2
xueb
xxf b
x
X
2.4 Gaussian r.v.
• A r.v. X is called Gaussian if its density function has the form
XX
ax
X
X aandwhereexf X
x
02
1)(
2
2
2
)(
2
x
a
X
X dexF X
x
2
2
2
)(
22
1)(
2.4 Gaussian r.v.
• Numerical or approximation methods for Gaussian r.v.– Tables many tables according to various
– Only one table according to normalized
– Consider
– For a negative value of x ;
– From FX(x), consider
XX a,
XX a,0,1 XX a
xx
a
X
X dexFdexF X
x
22
)(
2
2
2
2
2
1)(
2
1)(
)(1)( xFxF
XXXX
X aud
dua
u
,
X
XX
axFxF
)(
Example 2.4-1
• Find the probability of the event {X5.5} for Gaussian r.v. having aX = 3 and X = 2
8944.0)25.1()5.5(}5.5{
)(
)5.5(}5.5{
FFXP
axFxF
FXP
X
X
XX
X
Example 2.4-2
• Assume that the height of clouds at some location is Gaussian r.v. X with aX = 1830m and X = 460m
• Find the probability that clouds will be higher than 2750m
)(1)( xFxF
0228.0
9772.00.1
)0.2(1
460
183027501
)2750(1
}2750{1}2750{
F
F
F
XPXP
X
2.4 Gaussian r.v.
• Evaluations of F(X) by approximation
• Ex) 2.4-3 Gaussian r.v. with aX = 7, X = 0.5
x
dexQwherexQxF
2
2
2
1)()(1)(
02)1(
1
2
1)(
2
2
2
2
2
xe
bxaxadexQ
x
x
510.5,339.0 ba
7264.0251.5)6.0(339.0)6.0(661.0
11)6.0(1)6.0(
7257.0)6.0(5.0
73.7)3.7(}3.7{
2/)6.0(
2
2
e
QF
FFFXP X
Table B-1 : F(0.6)=0.7257
2.5 Other Distribution and Density
• Binomial distribution– Density function & distribution function
– Bernoulli trial– For N=6 and p=0.25
N
k
kNkX kxpp
k
Nxf
0
)()1()(
N
k
kNkX kxupp
k
NxF
0
)()1()(
,2,110 Nandp
2.5 Other Distribution and Density
• Poisson distribution– Density function & distribution function
– Quite similar to those for the binomial r.v.
– If N and p0 for the binomial case in such a way that Np=b, the Poisson case results
– Applications1. The number of defective units in sample taken from a production line
2. The number of telephone calls made during a period of time
3. The number of electrons emitted from a small section of cathode in a given time interval
– If the time interval of interest has duration T, and the events being counted are known to occur at an average rate and have a Poisson distribution, then b = T.
0b
00
)(!
)( ,)(!
)(k
kb
Xk
kb
X kxuk
bexFkx
k
bexf
2.5 Other Distribution and Density
• Uniform distribution
– The quantization of signal samples prior to encoding in digital communication systems
The error introduced in the round-off process
uniform distributed– Figure 2.5-2
elsewhere0
)(
1)(
bxaabxf X
xb
bxabx
axax
xFX
1
0
)(
2.5 Other Distribution and Density
• Exponential
ax
axebxf
b
ax
X
0
1)(
)(
ax
axebxF
b
ax
X
0
11)(
)(
2.5 Other Distribution and Density
• Rayleigh
– The envelope of one type of noise when passed through a bandpass filter
ax
axeaxbxf
b
ax
X
0
)(2
)(
2)(
ax
axexFb
ax
X
0
1)(
2)(
2.6 Conditional Distribution and Density Functions
• Conditional Probability– For two events A and B where P(B)0, the conditional
probability of A given B
• Conditional Distribution– A : identified as the event {X x} for the r.v. x– Conditional distribution function of X
– = the joint event– This joint event consists of all outcomes s such that
– The conditional distribution : discrete, continuous, or mixed random variables
)(
)()|(
BP
BAPBAP
)|()(
}{}|{ BxF
BP
BxXPBxXP X
}{ BxX BxX }{
BsandxsX )(
2.6 Conditional Distribution and Density Functions
• Properties of Conditional Distribution
)|()|()6(
)|()|(}|{)5(
)|()|()4(
1)|(0)3(
1)|()2(
0)|()1(
1221
2121
BxFBxF
BxFBxFBxXxP
xxifBxFBxF
BxF
BF
BF
XX
XX
XX
X
X
X
2.6 Conditional Distribution and Density Functions
• Conditional Density– Conditional density function of the r.v. x
• The derivative of the conditional distribution function
• If FX(x|B) contains discontinuities, impulse response are present in fX(x|B) to account for the derivatives at the discontinuities
– Properties
dx
BxdFBxf X
X
)|()|(
2
1
)|(}|{)4(
)|()|()3(
1)|()2(
)|(0)1(
21
x
x X
x
XX
X
X
dxBxfBxXxP
dBfBxF
dxBxf
xallBxf
2.6 Conditional Distribution and Density Functions
• Example 2.6-1– Red, green, and blue balls in two boxes as shown in the following
table– Select a box first, and then take a ball from the selected box– The 2nd box is larger than the 1st box causing the 2nd box to be
selected more frequently – Event selecting the 2nd box : B2, P(B2)=8/10
– Event selecting the 1st box : B1, P(B1)=2/10
– Event selecting a red ball : r.v. x1
– Event selecting a green ball : r.v. x2
– Event selecting a blue ball : r.v. x3 Box
Xi Ball color 1 2 Totals
1 Red 5 80 85
2 Green 35 60 95
3 Blue 60 10 70
Totals 100 150 250
2.6 Conditional Distribution and Density Functions
Box
Xi Ball color 1 2 Totals
1 Red 5 80 85
2 Green 35 60 95
3 Blue 60 10 70
Totals 100 150 250
15010
210060
1
15060
210035
1
15080
21005
1
)|3()|3(
)|2()|2(
)|1()|1(
BBXPBBXP
BBXPBBXP
BBXPBBXP
)3()2()1()|(
)3()2()1()|(
10060
10035
1005
1
10060
10035
1005
1
xuxuxuBxF
xxxBxf
X
X
2.6 Conditional Distribution and Density Functions
– total probability
173.0
)()|3()()|3()3(
390.0
)()|2()()|2()2(
437.0
)()|1()()|1()1(
108
15010
102
10060
2211
108
15060
102
10035
2211
108
15080
102
1005
2211
BPBXPBPBXPXP
BPBXPBPBXPXP
BPBXPBPBXPXP
)3(173.0)2(390.0)1(437.0)(
)3(173.0)2(390.0)1(437.0)(
xuxuxuxF
xxxxf
X
X
2.6 Conditional Distribution and Density Functions
• Event B : }{ bXB
)(
}{}|{)|(
)|()(
}{}|{
bXP
bXxXPbXxXPbXxF
BxFBP
BxXPBxXP
X
X
)()()( bXbXxX
1)(
)(
)(
}{}|{)|(
bXP
bXP
bXP
bXxXPbXxXPbXxFX
)()()( xXbXxX
)(
)(
)(
}{}|{)|(
bXP
xXP
bXP
bXxXPbXxXPbXxFX
Case 1. bx :
Case 2. b>x :
2.6 Conditional Distribution and Density Functions
– Conditional distribution
– From the assumption that the conditioning event has nonzero prob.,
– Similarly,
bx
bxdxxf
xf
bF
xf
bXxf
bx
bxbF
xFbXxF
b
X
X
X
X
X
X
X
X
0
)(
)(
)(
)(
)|(
1)(
)()|(
1)(0 bFX )()|( xFbXxF XX
bxxfbXxf XX )()|(
2.6 Conditional Distribution and Density Functions
• Example 2.6-2– Sky divers try to land within a target circle– Miss-distance from the point has the Rayleigh distribution wit
hb=800m2 and a=0
– Target : a circle of 50m radius with a bull’s eye of 10m radius– The prob. of sky diver hitting the bull’s eye, given that the la
nding is on the target ?
Sol) x = 10, b = 50
)(1)( 800
2
xuexFx
X
1229.0)1(
)1(
)50(
)10()50|10(
8002500
800100
e
e
F
FXF
X
XX
Random Poisson Points
• Papoulis pp. 117– Points in nonoverlapping intervals
• P{ka in ta, kb in tb}
)()|()( BPBAPBAP
where A = {ka in ta}, B = {kb in tb}
ta tb
0 T
bb kn
b
k
b
b T
t
T
t
k
nBP
1)(
Random Poisson Points
baa kkn
b
a
k
b
a
a
b
tT
t
tT
t
k
knBAP
1)|(
bababkkn
b
a
kn
b
k
b
a
k
b
a
b
b tT
t
T
t
tT
t
T
t
k
kn
k
nBAP
11)(
baba kkn
ba
k
b
k
a
baba T
t
T
t
T
t
T
t
kknkk
n
1
)!(!!
!
bb kn
b
k
b
b T
t
T
t
k
nBP
1)(
Homework
• Prob. 2.3-1, 2.3-5, 2.3-9, 2.3-12, 2.3-13, 2.4-3, 2.4-4, 2.5-7
• Due : next Tuesday