Poisson cumulative distribution function
p = poisscdf(x,lambda)
p = poisscdf(x,lambda,'upper')
p = poisscdf(x,lambda) returns
the Poisson cdf at each value in
x using the corresponding
mean parameters in
be vectors, matrices, or multidimensional arrays that have the same
size. A scalar input is expanded to a constant array with the same
dimensions as the other input. The parameters in
p = poisscdf(x,lambda,'upper') returns
the complement of the Poisson cdf at each value in
using an algorithm that more accurately computes the extreme upper
The Poisson cdf is
For example, consider a Quality Assurance department that performs random tests of individual hard disks. Their policy is to shut down the manufacturing process if an inspector finds more than four bad sectors on a disk. What is the probability of shutting down the process if the mean number of bad sectors ( ) is two?
probability = 1-poisscdf(4,2)
probability = 0.0527
About 5% of the time, a normally functioning manufacturing process produces more than four flaws on a hard disk.
Suppose the average number of flaws ( ) increases to four. What is the probability of finding fewer than five flaws on a hard drive?
probability = poisscdf(4,4)
probability = 0.6288
This means that this faulty manufacturing process continues to operate after this first inspection almost 63% of the time.