This presentation shows the history and the events the led to practical application of Poisson's Distribution

1. APPLICATION OF POISSON
DISTRIBUTION
By FURQAN JAVEED SYED
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2. INTRODUCTION
What do radioactive
decay and winning
the lottery have in
common?
They, and a lot of
other phenomena, are
described well by the
Poisson Distribution!
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3. HISTORY
• The Poisson Distribution was first introduced
by a French mathematician Siméon Dennis
Poisson (1781-1840) in 1837.
• He published it along with probability theory
in his work on “Research on Probability of
Judgements in Criminal and Civil Matters”.
• The work theorized about the number of
wrongful convictions in each country by
focusing on a certain random variable N that
count, among other things, the number of
discrete occurrences that take place during a
time interval of given length.
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4. WHAT IS
POISSON
DISTRIBUTION?
• The Poisson distribution is a discrete
probability distribution for the counts
of events that occur randomly in each
interval of time [or space]
• Many experimental situations occur in
which we observed the counts of events
within a set unit of time, area, volume,
length, etc.
Examples:
• The number of cases of a disease in
different towns.
• The number of mutations in a set sized
region of a chromosome.
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5. POISSON DISRTIBUTION EQUATION
Poisson distribution is for counts—if events happen at a constant rate over time, the
Poisson distribution gives the probability of X number of events occurring in time T.
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6. APPLICATION OF
POISSON DISTRIBUTION
A practical application of this
distribution was made by
LADISLAUS BORTKIEWICZ in 1898
when he was given the task of
investigating the number of
soldiers in the Russian army
killed accidentally by horse kicks.
This experiment introduced
Poisson’s distribution to the field
of reliability engineering.
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7. APPLICATIONS OF POISSON DISTRIBUTION
Birth defects and
genetic mutation.
Rare diseases (like
leukemia, but not aids
because it’s infectious
and not independent) –
especially in legal cases.
Number of deaths per
day or week due to a
rare disease in a big
hospital.
Traffic flow and ideal
distance gap.
Number of typing errors
on a page.
Hairs found in
McDonald’s hamburger
The count of alpha
particles emitted per
unit of time is useful in
analysis of any
radioactive substance.
Failure of a machine in
a month.
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8. THANK YOU
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