     # Ch2 (Review of Probability)

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A random variable is a number chosen atrandom as the outcome of an experiment.

Random variable may be real or complex and

may be discrete or continuous. In S.P. ,the random variable encounter are most

often real and discrete.

We can characterize a random variable by itsprobability distribution orby itsprobability densityfunction (pdf).

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The distribution function for a random variable yis the probability that y does not exceed somevalue u,

and

3

)()( uyPuFy

)()()( uFvFvyuP yy

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Theprobability density function is the derivativeof the distribution:

and,

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)()( uF

du

duf yy

v

uy dyyfvyuP )()(

1)(

yF

1)(

dyyfy

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We can also characterize a random variable byits statistics.

The expected value of g(x) is written E{g(x)} or

and defined as Continuous random variable:

Discrete random variable:

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dxxfxgxg )()()(

x

xpxgxg )()()(

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The statistics of greatest interest are the momentofp(x).

The kth moment ofp(x) is the expected value of

. For a discrete random variable:

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k

x

x

kk

k

xpxxm )(

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The first moment, , is the mean of x.

Continuous:

Discrete:

The second central moment, also known as the

variance of p(x), is given by

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1m

x

xxpxx )(

dxxxfx )(

2

2

22

)()(

xm

xpxxx

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To estimate the statistics of a random variable,we repeat the experiment which generates thevariable a large number of times.

If the experiment is run Ntimes, then each value xwill occur Np(x) times, thus

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N

i

ix xN 1

1

N

i

k

ik xNm 1

1

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A random variable has a uniform density onthe interval (a, b) if :

9

otherwise,0

),/(1

)(

bxaab

xfx

bx

bxaabax

ax

xFx

,1

),/()(

,0

)(

22 )(12

1ab

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The gaussian, or normal, density function isgiven by:

22 2/)(

2

1),;(

xexn

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If two random variables x and y are to beconsidered together, they can be described interms of theirjoint probability densityf(x, y) or,

for discrete variables,p(x, y).

Two random variable are independent if

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)()(),( ypxpyxp

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Given a functiong(x, y), its expected value isdefined as: Continuous:

Discrete:

And joint moment for two discrete random variable is:

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dxdyyxfyxgyxg ),(),(),(

yx

yxpyxgyxg,

),(),(),(

yx

ji

ij yxpyxm,

),(

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Moments of a random variable X

][].[][

)(][

)(),(),(][

),(][

,,

,

YEXEXYE

dyyyfYE

dxxxfdydxyxfxdxdyyxxfXE

dxdyyxfyxyxE

y

XYXYX

YX

nnnn

If X and Y are independents and in this case

)().(),(, yfxfyxf YXYX

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.. .

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Two random variables x and y are jointlygaussian if their density function is :

Where

22

yx

xyxyr

2

2

2

2

222

)1(21exp

121),(

yyxxyx

yrxyxrr

yxn

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A random function is one arising as theoutcome of an experiment.

Random function need not necessarily be

functions of time, but in all case of interest to usthey will be.

A discrete stochastic process is characterizedby many probability density of the form,

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),...,,,,,...,,,( 321321 nn ttttxxxxp

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If the individual values of the random signalare independent, then

If these individual probability densities are allthe same, then we have a sequence of

independent, identically distributed samples(i.i.d.).

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),()...,(),(),...,,,,...,,( 22112121 nnnn txptxptxptttxxxp

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Mean and autocorrelation can be determined intwo ways:

The experiment can be repeated many times and the

average taken over all these functions. Such anaverage is called ensemble average. Take any one of these function as being

representative of the ensemble and find the averagefrom a number of samples of this one function. Thisis called a time average.

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If the time average and ensemble average of arandom function are the same, it is said to beergodic.

A random function is said to be stationary if itsstatistics do not change as a function of time.

Any ergodic function is also stationary.

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In stationary signal we have:

Where

And the autocorrelation function is :

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xtx )(),,(),,,( 212121 xxpttxxp

12 tt

21 ,2121 ),,()( xx xxpxxr

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When x(t) is ergodic, its mean andautocorrelation is :

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N

NtN

txN

x )(2

1lim

)()(1

lim)()()(

N

NtN

txtxN

txtxr

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The cross-correlation of two ergodic randomfunctions is :

The subscript xy indicates a cross-correlation.

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N

NtN

xy tytx

N

tytxr )()(1

lim)()()(

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The Fourier transform of (theautocorrelation function of an ergodic randomfunction) is called thepower spectral density of

x(t) :

The cross-spectral density of two ergodic randomfunction is :

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jerS )()(

j

xyxy erS )()(

)(r

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For ergodic signal x(t), can be written as:

Then from elementary Fourier transform properties,

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2|)(|

)()(

)()()(

X

XX

XXS

)(r

)()()( xxr

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If all values of a random signal areuncorrelated,

Then this random function is called white noise The power spectrum of white noise is constant,

White noise is mixture of all frequencies.

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)()( 2 r

2)( S

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