chapter 8 probability and random variables
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Chapter 8 Probability and Random variables. F. G. Stremler , Introduction to Communication Systems 3/e. Probability All possible outcomes (A 1 to A N ) are included Joint probability Conditional probability. Bayes ’ theorem - PowerPoint PPT PresentationTRANSCRIPT
Chapter 8 Probability and Random variables
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F. G. Stremler, Introduction to Communication Systems 3/e
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• Probability • All possible outcomes (A1 to AN) are included
• Joint probability
• Conditional probability
NNAP A
N lim)(
1)(1
N
iiAP
NNABP AB
N lim)(
)()(
/)|(
APABP
NNNN
NNABP
A
AB
A
AB )()(
/)|(
BPABP
NNNN
NNBAP
B
AB
B
AB
)()|()()|()( BPBAPAPABPABP
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• Bayes’ theorem
• Random 2/52 playing cards. After looking at the first card, P(2nd is heart)=? if 1st is or isn’t heart
A: a heart on the 1st; B: a heart on the 2nd; C: no heart on the 1st
P(B|A) = 12/51; P(B|C) = 13|51• Probability of two mutually exclusive events
P(A+B)=P(A)+P(B)• If the events are not mutually exclusive
P(A+B)=P(A)+P(B)-P(AB)
)()|()()|(
APBAPBPABP
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Random variables• A real valued random variable is a real-value
function defined on the events of the probability system.
• Cumulative distribution function (CDF) of x is
• Properties of F(a)• Nondecreasing, • 0≤F(a)≤1,
)(lim)()(nanaxPaF x
n
1)(0)(
FF
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Probability density function (PDF)
xadaadFxf |)()(
Properties of PDF
.0)( xf
1)()(
Fdxxf
aXPdxaf x )(
)()( axPaF
Tutorial Q.2 • Consider the experiment that consists in the rolling of two
honest dice. The random variable X is assigned to the sum of the numbers showing up to the two dice. Determine and plot the cumulative distribution function (CDF) and the probability distribution function (pdf) of X.
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Discrete and continuous distributions• Discrete: random variable has M discrete values
CDF or F(a) was discontinuous as a increase.Digital communicationsPDF
CDF
events discretely ofnumber theisM
)()()(1
M
iii xxxPxf
MLax
xPaF
L
L
ii
, that suchinteger largest theis L
)()(1
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• Continuous distributions: if a random variable is allowed to take on any value in some interval.
CDF and PDF would be continuous functions.
Analogue communications, noise.• Expected value of a discretely distributed random
variable
)()()]([1
i
M
ii xPxhxhy
Normalized average power
P = i
yi2 p(yi)
exampleA discrete random signal, y(t), can take one of the
four predefined voltage levels, y1 = 0.5 V, y2 = 0.4 V, y3 = 0.2 V, and y4 = 0.1 V. Assume that these levels occur with probabilities, p(y1) = 0.2, p(y2) = 0.3, p(y3) = 0.1, and p(y4) = 0.4.
Calculate the average power delivered by y(t) into a 100Ω resistive load.
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The normalized average power, P, is given by:
P = i
yi2 p(yi)
i.e. (0.5)2 0.2 + (0.4)2 0.3 + (0.2)2 0.1 + (0.1)2 0.4 = 0.106/100 W = 1.06 mW
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Important distributions• Binomial : discrete
• Poisson: discrete
• Uniform: continuous, a random variable that is equally likely to take on any value within a given range.
• Gaussian (normal): continuous• Normalised Gaussian pdf having zero mean and
unit variance
• Sinusoidal: continuous
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2/2
21)( xexp
13