This is documentation for Mathematica 8, which was
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# PoissonDistribution

 PoissonDistribution[] represents a Poisson distribution with mean .
• The probability for integer value in a Poisson distribution is for . »
Probability density function:
Cumulative distribution function:
Mean and variance of a Poisson distribution are the same:
Probability density function:
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Cumulative distribution function:
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Mean and variance of a Poisson distribution are the same:
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 Scope   (7)
Generate a set of pseudorandom numbers that are Poisson distributed:
Compare its histogram to the PDF:
Distribution parameters estimation:
Estimate the distribution parameters from sample data:
Compare the density histogram of the sample with the PDF of the estimated distribution:
Skewness:
A Poisson distribution becomes symmetric for large values of :
Kurtosis:
Kurtosis of normal distribution is the limiting value for kurtosis of a Poisson distribution:
Different moments with closed forms as functions of parameters:
Closed form for symbolic order:
Closed form for symbolic order:
All cumulants equal :
Hazard function:
Quantile function:
 Applications   (14)
CDF of PoissonDistribution is an example of a right-continuous function:
The average number of accidents in a city is 100 per day. Simulate the daily accidents:
Find the probability that there are 90 or more accidents on a single day:
Find the standard deviation of the number of accidents per day:
The expected number of raindrops falling into a bucket in a 5-second interval is 20. Simulate the raindrop count for each 5-second interval:
Find the probability that exactly 20 raindrops fall into the bucket in 5 seconds:
A radioactive material on average emits 3.2 -particles per second; show the distribution:
Compute the probability that more than 4 -particles are emitted over the next second:
Simulate a typical particle count per second over 10 minutes:
Suppose flaws in plywood occur on average of one flaw per 50 square feet. Simulate the process of finding flaws on a per-square-foot basis:
Find the probability that a 4-foot-by-8-foot sheet will have no flaws:
For a mirror with area 7.54 cm, the probability of no flaws is 0.9100. Using the same polishing process, another mirror with an area of 19.50 cm is fabricated. Assuming a Poisson error process, find the probability of no flaws on the larger mirror. Using the conditions for the smaller mirror and with error distribution :
The resulting mirror polishing defect distribution is then:
The probability of no errors in the larger mirror is given by:
Typographical errors in a book are occurring randomly according to a Poisson process. On 384 pages, 158 errors are counted. Find the distribution of errors per page where the distribution is of the form PoissonDistribution, where p is the number of pages:
Find the probability of exactly 0 errors per page:
Find the probability of fewer than 2 errors per page:
Find the probability of 1 or more errors per page:
In modeling an adverse reaction to a medicine, it is determined that on average 2 people per 100000 have the adverse reaction. Assuming a Poisson distribution, find the adverse reaction distribution:
Find the probability that there will be at least 5 adverse reactions when the medicine is administered to 350000 people:
The number of queries arriving in seconds to a call center is Poisson distributed with parameter , where is the average arrival rate in queries per second. Assuming that the average arrival rate is 4 queries per minute, find the probability of getting more than 4 queries in 10 seconds:
Find the probability that there are fewer than 5 queries in 2 minutes:
The number of packet arrivals in seconds at a multiplexer is Poisson distributed with parameter , where is the average arrival rate in packets per second. Find the probability that there are no packet arrivals in seconds:
Find the probability that or fewer packets arrive in seconds:
A data center has 10000 disk drives. Suppose that a disk fails in a given day with probability . Find the probability that there are no failures in a given day:
That distribution is closely approximated with a Poisson distribution with parameter :
Find the probability that there are fewer than 10 failures in 2 days:
Find the number of spare disk drives that should be available so that all failures in a day can be replaced with probability 99.9%:
Plot the cumulative probability that the number of failures is less than :
Find the minimum number for which the probability is greater than 99.9%:
Check the result:
An LCD display has 1920×1080 pixels. A display is accepted if it has 15 or fewer faulty pixels. The probability that a pixel is faulty from production is . Find the proportion of displays that are accepted:
Find the pixel failure rate required to produce 4000×2000 pixel displays and still have an acceptance rate of at least 90%:
Plot the acceptance rate as a function of the pixel failure rate:
Find the maximal acceptable pixel failure rate:
Check the result:
A switchboard receives on average 100 calls per minute. What should the switchboard capacity be so that it gets saturated less than once in every 60 minutes?
Find the minimum capacity that satisfies the constraint:
In an optical communication system, transmitted light generates current at the receiver. The number of electrons follows the parametric mixture of a Poisson distribution and another distribution, depending on the type of light. If the source uses coherent laser light of intensity , then the electron count distribution is Poisson:
If the source uses thermal illumination, then the Poisson parameter follows ExponentialDistribution with parameter and the electron count distribution is:
These two distributions are distinguishable and allow you to determine the type of source:
The sum of Poisson variables is Poisson distributed:
Relationships to other distributions:
PoissonDistribution is a limiting case for BinomialDistribution with :
PoissonConsulDistribution simplifies to Poisson distribution:
Poisson distribution is a limiting case for PolyaAeppliDistribution:
The parametric mixture of Poisson distribution and ExponentialDistribution follows GeometricDistribution:
The limit of NegativeBinomialDistribution when the mean is fixed is Poisson distribution:
For large , the PoissonDistribution can be approximated by NormalDistribution:
PoissonDistribution is not defined when is non-positive:
Substitution of invalid parameters into symbolic outputs gives results that are not meaningful:
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