Transcript Slides (Dr. Zaruba) - The University of Texas at Arlington

The University of Texas at Arlington
Topics in Random Processes
CSE 5301 – Data Modeling
Guest Lecture: Dr. Gergely Záruba
Poission Process
• The Poission process is very hand when modeling
birth/death processes, like arrival of calls to a telephone
system.
• We can approach this by discretizing time, and assuming
that in any time slot an event is happening with
probability p. However two events cannot happen in the
same time slot. Thus we need to reduce the size of the
time slots to infinitesimally small slots.
• Thus, we need to talk about the rate (events/time).
• Number of events in a given time duration is thus
Poisson distributed
• The time between consecutive events (inter-arrival time)
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is inverse-exponentially distributed.
Heavy Tails
• A pdf is a function that is non-negative and for which the indefinit
integral is 1.
• We have looked at pdf-s that decay exponentially (e.g., gaussian,
inverse-exponential)
• Can we create a pdf-like function that decays like 1/x ? - Although
the function converges to 0, the integral of the function has a
logarithmic flavor and thus will never integrate to 1.
• Can we create a pdf-like function that decays like x^-2? - The
integral of the function has 1/x flavor and thus can work as a pdf.
However, when calculating the mean the integral turns the function
again to a divergent function. Thus this pdf has an infinite mean.
• Similarly, we can see, that a x^-3 like decay can result in a pdf with
a finite mean but infinite variance.
• These latter two pdfs are good examples of heavy tailed
distributions! Unfortunately, many processes have heavy-tailed
features.
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Heavy Tail’s Impact on CLT
• For the Central Limit Theorem to work, the distributions
from which the random numbers added are generated
have to have finite means and variances.
• Thus, if the underlying distribution from which the
samples come is heavy tailed, then we cannot use
significance testing and confidence intervals.
• Unfortunately, it has been shown that Internet traffic can
be modeled by a Pareto distribution most closely. Thus if
traffic is modeled this way, then significance of results
cannot be established.
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Random Numbers in Computers
• Truly random numbers in proper digital logic do not exist.
• We can use algorithms that generate sequences of numbers that
have statistical properties as if they were generated by a uniform
distribution.
• Pseudo-random and quazi-random number generators.
• Pseudo-random number generators generate a random looking
integer between 1 and MAX_RAND.
• They are actually sequences of numbers, where the seed is is the first
number in the series.
• This lack of randomness is actually great for computer simulations:
our experiments can be repeated deterministically. We set the seed
usually once at the beginning. Different experiments are created by
setting different seeds.
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Random Variate Generation
• So, we can generate uniform random numbers. By
dividing by MAX_RAND we have a semi-continous
random between (0,1).
• How can we use this to generate random numbers of
different distributions?
• We need to obtain the cdf of the distribution and invert it.
Then substituting the (0,1) uniform random into the
inverse cdf we get the properly distributed random
number.
• As an example, the inverse exponential distribution’s
inverse cdf is: -log(1-u)/lambda (where u is the (0,1)
random)
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How about Generating Normal?
• Unfortunately, the normal distribution does not have a
closed form for its inverse cdf (and the normal is not the
only such distribution)
• We could sum up a large number (>30) of uniform
randoms and scale it to the proper mean and variance.
• There are other approximations, usually based on
trigonometric functions.
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Resolution of Random Numbers
and Their Impact
• When we create rand()/MAX_RAND, we end up with a
floating point number. However not only is this number a
rational number (we cannot have irrational numbers in
computers), its values are never closer than
1/MAX_RAND.
• Thus, when substituting into the inverse cdf, the shape of
the inverse cdf will determine how close the final
random variates’ values will be.
• This is especially significant of a problem for heavy tailed
distributions, where theoretically there are larger
chances to create very small or very large numbers,
which may be prohibited from generation by the
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resolution of the (0,1) random.
Case Study: Aloha
• The Aloha networking protocol was used to connect
computers distributed over the Hawaiian islands with a
shared bandwidth wireless network.
• The medium (the radio bandwidth) had to be shared by
many radios. The task is to come up with effective and
fair ways to share.
• Aloha was the first approach and it is characterized by
the lack of an approach. As soon as data is ready to be
transmitted, a radio node is going to transmit it.
• This is a straight forward application of a Poission
process.
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Case Study: Aloha
• Let us assume, that any time a node transmits, it is going to transmit
for T duration. Let us assume that the arrival of this transmission
events follows a Poission process (it is a straight forward
assumption).
• Let us assume that a node starts transmitting at time t0. In order for
this to be successful, no other node should transmit in the time
period (t0-T,t0+T), i.e., the collision domain is 2*T.
• What is the utilization of this network?
• The rate of arrival is l. This is our best guess (the mean of the
Poisson) of how many arrivals happen in a time frame.
• What I then the probability that none of the other nodes transmit for
2T duration? It is a Poisson with (2T,0).
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Case Study: Aloha
• Thus it is: l^0 * e^(-2l) / 0! Which is e^-2l
• Thus the total utilization is: l*e^-2l
• After differentiating this we will find its maximum to be at
l=0.5 where the value is 1/(2e)= 18%
• This is not great.
• We can easily double the performance by synchronizing
the network (slotted-Aloha) and making sure that nodes
can only start transmitting at time instances that are
integer multiples of T.
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Revisiting Confidence Intervals
• We have a random process that generates random numbers with an
underlying mean m and standard deviation s .
• We take N samples. Based on this N samples we can calculate the
sample mean m and unbiased sample standard deviation s.
• We want to claim a C% confidence (e.g., C=0.95)
• We want to say something about the relationships of m and m, more
precisely we want the Pr(m-e< m <m+e)>C
• The interval (m-e,m+e) is our confidence interval
• The error e is: e= z(a)*s/N if we have taken enough samples
(N>30) or if we know that the underlying distribution is Gaussian.
(otherwise, we have to calculate with t(a,N) ). z(a) is the a-th
percentile of the standard normal (e.g., for a 95% confidence it
would be 1.96)
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Being Confident the Relative Way
• Unlike social science statisticians, using computer simulations we
usually do not have a limit on our sample size. We can always run
more simulations (time permitting) with different seeds.
• We can draw our data points in figures with confidence bars over
them, but this can result in messy figures.
• Sometimes it is better to just show our best estimate but making
sure that we bound the error.
• Thus we can talk about figures having a confidence value and
bound relative errors. Our relative error is the error e divided by the
best guess m. er=e/m
• We should run enough simulations (large enough N) to claim a large
confidence that our relative error is less than a small number (in
engineering 95% confidence with 5% relative error is usually an
acceptable minimum)
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Being Confident; the Recipe
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•
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Make sure that simulation does not model heavy tailed distributions and that
you have an argument that the underlying random variable has a finite
mean ad variance.
Set N0=30 (if you do not know that the underlying is a Gaussian)
Run the simulation N0 times with N0 different seeds.
Calculate the mean m0, the standard deviation s0, the error e0, and the
relative error er0 based on a chosen confidence level C. If er0 less than the
target relative error ert(e.g., 5%) then you are done with the data point.
If not, you need to estimate how many simulation runs you need to be
confident. This can be done based on the current data. Assume that the
mean and the standard deviation are not going to change to the worse, then
you can calculate the target error (et0< m0 * ert).
Turning the error’s function around: N= (z(a)*s/e)^2 we can estimate the
number of samples needed by: N1= (z(a)*s0/et0)^2
Throw out the data points, chose N1 different seeds (different than before)
and repeat the experiment from bullet point 3. Iterate as long as bullet point
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4 checks out (which is likely going to be your second iteration).
Case Study: Aloha, a Simulation
• Look at the main code in the Aloha simulation example.
• The code corresponds to the previous recipe.
• Try to enter simulation parameters that will not result in
confidence after 30 runs.
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