math 256 probability and random processes
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OKAN UNIVERSIT Y FACULTY OF ENGINEERING AND ARCHITECTURE. MATH 256 Probability and Random Processes. 04 Random Variables. Yrd . Doç . Dr. Didem Kivanc Tureli [email protected] [email protected]. Fall 2011. Probability Mass Function. Is defined for a discrete variable X. - PowerPoint PPT PresentationTRANSCRIPT
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MATH 256 Probability and Random Processes
Yrd. Doç. Dr. Didem Kivanc [email protected]
14/10/2011 Lecture 3
OKAN UNIVERSITYFACULTY OF ENGINEERING AND ARCHITECTURE
04 Random Variables
Fall 2011
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Probability Mass Function
• Is defined for a discrete variable X.
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p a P X a
0 1,2,...( ) 0
ip x ip a
p x x
for for all other values of
1
1ii
p x
• Suppose that
• Then since x must be one of the values xi,
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Example of probability mass function
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0 0 1 4
1 1 1 2
2 2 1 4
p P X
p P X
p P X
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Expectation of a random variable• If X is a discrete random variable having a probability mass
function p(x) then the expectation or the expected value of X denoted by E[X] is defined by
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: ( ) 0
( )x p x
E X xp x
• In other words, • Take every possible value for X• Multiply it by the probability of getting that value• Add the result.
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Examples of expectation• For example, suppose you have a fair coin. You flip the coin,
and define a random variable X such that – If the coin lands heads, X = 1
– If the coin lands tails, X = 2
• Then the probability mass function of X is given by
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11 2
2p p 1 2 if 1 or 2,
0 otherwise.x x
p x Or we can write
1 11 2 1.5
2 2E X
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Expectation of a function of a random variable
• To find E[g(x)], that is, the expectation of g(X)• Two step process: – find the pmf of g(x)– find E[g(x)]
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Let X denote a random variable that takes on any of the values –1, 0, and 1 with respective probabilities
{ 1} 0.2 { 0} 0.5 { 1} 0.3P X P X P X Compute 2E X
SolutionLet Y = X 2.
{ 1} { 1} { 1} 0.5{ 0} { 0} 0.5
P Y P X P XP Y P X
2 1(0.5) 0(0.5) 0.5E X E Y
Then the probability mass function of Y is given by
0.5 if 0 or 10 otherwise.
y yp y
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Proposition 4.1
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If X is a discrete random variable that takes on one of the values xi, i ≥ 1 with respective probabilities p(xi), then any real valued function g.
i ii
E g X g x p x Check if this holds for the previous example:
22 2 21 0.2 0 0.5 1 0.3
1 0.2 0.3 0 0.50.5
E X
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Proof of Proposition 4.1
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:
:
:
i j
i j
i j
i i i ii j i g x y
j ij i g x y
j ij i g x y
j jj
g x p x g x p x
y p x
y p x
y P g X y
E g X
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Variance
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Variance
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Some more comments about variance
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Bernoulli Random Variables
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Binomial Random Variables
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Poisson Random Variable
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Some examples of random variables that generally obey the Poisson probabilitylaw [that is, they obey Equation (7.1)] are as follows:1. The number of misprints on a page (or a group of pages) of a book2. The number of people in a community who survive to age 1003. The number of wrong telephone numbers that are dialed in a day4. The number of packages of dog biscuits sold in a particular store each day5. The number of customers entering a post office on a given day6. The number of vacancies occurring during a year in the federal judicial system7. The number of α-particles discharged in a fixed period of time from some radioactivematerial
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Expectation of the sum of r.v.s• Start with this:
• It’s really quite easy to show that:
• And from this we show that
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