Mixed Poisson distribution

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A mixed Poisson distribution is a univariate discrete probability distribution in stochastics. It results from assuming that the conditional distribution of a random variable, given the value of the rate parameter, is a Poisson distribution, and that the rate parameter itself is considered as a random variable. Hence it is a special case of a compound probability distribution. Mixed Poisson distributions can be found in actuarial mathematics as a general approach for the distribution of the number of claims and is also examined as an epidemiological model.[1] It should not be confused with compound Poisson distribution or compound Poisson process.[2]

Quick Facts Notation, Parameters ...
mixed Poisson distribution
Notation
Parameters
Support
PMF
Mean
Variance
Skewness
MGF , with the MGF of π
CF
PGF
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Definition

Summarize
Perspective

A random variable X satisfies the mixed Poisson distribution with density π(λ) if it has the probability distribution[3]

If we denote the probabilities of the Poisson distribution by qλ(k), then

Properties

Summarize
Perspective

In the following let be the expected value of the density and be the variance of the density.

Expected value

The expected value of the mixed Poisson distribution is

Variance

For the variance one gets[3]

Skewness

The skewness can be represented as

Characteristic function

The characteristic function has the form

Where is the moment generating function of the density.

Probability generating function

For the probability generating function, one obtains[3]

Moment-generating function

The moment-generating function of the mixed Poisson distribution is

Examples

Summarize
Perspective

TheoremCompounding a Poisson distribution with rate parameter distributed according to a gamma distribution yields a negative binomial distribution.[3]

Proof

Let be a density of a distributed random variable.

Therefore we get

TheoremCompounding a Poisson distribution with rate parameter distributed according to an exponential distribution yields a geometric distribution.

Proof

Let be a density of a distributed random variable. Using integration by parts n times yields: Therefore we get

Table of mixed Poisson distributions

More information mixing distribution ...
mixing distribution mixed Poisson distribution[4]
Dirac Poisson
gamma, Erlang negative binomial
exponential geometric
inverse Gaussian Sichel
Poisson Neyman
generalized inverse Gaussian Poisson-generalized inverse Gaussian
generalized gamma Poisson-generalized gamma
generalized Pareto Poisson-generalized Pareto
inverse-gamma Poisson-inverse gamma
log-normal Poisson-log-normal
Lomax Poisson–Lomax
Pareto Poisson–Pareto
Pearson’s family of distributions Poisson–Pearson family
truncated normal Poisson-truncated normal
uniform Poisson-uniform
shifted gamma Delaporte
beta with specific parameter values Yule
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References

Further reading

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