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Lecture 14 Simulation and Modeling. Md. Tanvir Al Amin, Lecturer, Dept. of CSE, BUET. CSE 411. Discrete Uniform Distribution. Uniform distribution inside a interval. Say a random variable is equally likely to take value between i and j inclusive What is the probability that X = x where - PowerPoint PPT Presentation
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LECTURE 14SIMULATION AND MODELINGMd. Tanvir Al Amin, Lecturer, Dept. of CSE, BUETCSE
411
Discrete Uniform Distribution Uniform distribution inside a interval.
Say a random variable is equally likely to take value between i and j inclusive
What is the probability that X = x where
Mean
Variance
jxi
11)(
ij
xp
2)( ji
121)1( 2 ij
Discrete Uniform Distribution
Probability Mass Probability Distribution
otherwise ,0
},.....1,{ x if ,1
1)( jii
ijxP
jx
jxiijix
ix
xF
if , 1
if ,11
if , 0
)(
Binomial Distribution Number of successes in n independent
Bernoulli trials with probability p of success in each trial
Relation between bernoulli and binomial : Suppose a two-tailed experiment
Pick a ball from the urn : Ball is either blue or red So two tailed test Pr { Blue } = 6/10 = 0.6 Pr { Red } = 4/10 = 0.4 This is a bernoulli trial
Binomial Distribution Now suppose we have n such urns…
Pick a ball from urn 1, Pick a ball from urn 2, Pick a ball from urn 3…
All are independent events. Each of these parallel experiments have Pr{red}=0.4, and Pr{blue} = 0.6
Urn 1 Urn 2 Urn 3 Urn 4 Urn 5
Binomial Distribution All are independent events. Each of
these parallel experiments have Pr{red}=0.4, and Pr{blue} = 0.6
Does it mean, all of these experiments will have same outcome ?
NO !!!
One Experiment
2 red, 3 blue balls …
Another Experiment
4 red, 1 blue balls …
Binomial Distribution What is the probability that outcome is 1
red ball ? i.e. (4 blue balls) What is the probability that outcome is 3
red balls ? (and hence 2 blue balls)
Answer : Binomial Distribution…probability of x success in n independent two tailed tests ….
xnx ppxn
xp
)1()(
Binomial Dist.
Mass function for various value of p
n = 15n = 5P = 0.9, 0.5, 0.2
Binomial Distribution
Distribution
Binomial Distribution Mean Variance If Y1, Y2, … Yn are independent bernoulli
RV and Y is bin(n,p) then Y = Y1 + Y2 + …. Yn
If X1, X2… Xm are independent RV and Xi ~ bin(ni,p) then X1 + X2 + … + Xm ~ bin(t1+t2+…….tm, p)
np
)1( pnp
Binomial Distribution The bin(n,p) distribution is symmetric if
and only if p=1/2 X~ bin(n, p) if and only if X ~ bin (n, 1-p) The bin(1,p) and Bernoulli(p)
distributions are same
Geometric Distribution Number of failures before first success in
a sequence of independent Bernoulli trials with probability p of success on each trial…
The probability distribution of the number X of Bernoulli trials needed to get one success…
Geometric Distribution From previous example
Say blue ball = failure Say red ball = success Say we have infinite urns.
Step 1 C = 0 Step 2 Take a new urn Step 3 We pic one ball Step 4 If the ball is red, we are done … Print C
Else If the ball is blue C = C + 1, goto step 2 Now, what is the probability that C will be 5
?? Or 3 ?? Or 0 ??
Geometric Distribution Probability of x failures
= x blue balls followed by 1 red ball So
otherwise ,0
0 if ,)1()(
xppxp
x
x times failure(1-p) to the power xFollowed by 1
success
Geometric Distribution Mean
Variance
MLE :
pp1
2
1pp
1)(1
nXp
Geometric Distribution If X1, X2 … Xs are independent geom(p)
random variables, then X1 + X2 + … + Xs has a negative binomial distribution with parameters s and p
The geometric distribution is the discrete analog of the exponential distribution, in the sense that it is the only discrete distribution with the memoryless property.
The geom(p) distribution is a special case of the negative binomial distribution (with s=1 and the same value for p)
Negative Binomial Distribution Number of failures before the s-th
success in a sequence of independent bernoulli trials with probability p of success on each trial.
Number of good items inspected before encountering the s-th defective item
Number of items in a batch of random size
Number of items demanded from an inventory
Negative Binomial Distribution
Mean :
Variance:
otherwise ,0
0 xif ,)1(1
)(xs pp
xxs
xp
pps )1(
2
)1(pps
Negative Binomial Distribution
Poisson Distribution Number of events that occur in an
interval of time when the events are occuring at a constant rate
Number of items in a batch of random size
Number of items demanded from an inventory
Poisson Distribution
Mean : Variance: MLE :
otherwise ,0
0 if ,!)( xx
exp
x
)(nX
Poisson Distribution If Y1, Y2 …. be a sequence of non
negative IID random variables and let Then the distribution of the Yi‘ If and only if X ~ Poisson(λ)
}1:max{1
i
j
YjiX
}1{exp o
Poisson Distribution If X1, X2, … .Xm are independent Random
variables and Xi ~ Poisson (λi), Then X1+ X2 + X3 …. Xm ~ Poisson (λ1 +λ2 … +λm)