Mixed binomial process

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A mixed binomial process is a special point process in probability theory. They naturally arise from restrictions of (mixed) Poisson processes bounded intervals.

Definition

Let P be a probability distribution and let Xi,X2, be i.i.d. random variables with distribution P. Let K be a random variable taking a.s. (almost surely) values in ={0,1,2,}. Assume that K,X1,X2, are independent and let δx denote the Dirac measure on the point x. Then a random measure ξ is called a mixed binomial process iff it has a representation as

ξ=i=0KδXi

This is equivalent to ξ conditionally on {K=n} being a binomial process based on n and P.[1]

Properties

Laplace transform

Conditional on K=n, a mixed Binomial processe has the Laplace transform

(f)=(exp(f(x))P(dx))n

for any positive, measurable function f.

Restriction to bounded sets

For a point process ξ and a bounded measurable set B define the restriction ofξ on B as

ξB()=ξ(B).

Mixed binomial processes are stable under restrictions in the sense that if ξ is a mixed binomial process based on P and K, then ξB is a mixed binomial process based on

PB()=P(B)P(B)

and some random variable K~. Also if ξ is a Poisson process or a mixed Poisson process, then ξB is a mixed binomial process.[2]

Examples

Poisson-type random measures are a family of three random counting measures which are closed under restriction to a subspace, i.e. closed under thinning, that are examples of mixed binomial processes. They are the only distributions in the canonical non-negative power series family of distributions to possess this property and include the Poisson distribution, negative binomial distribution, and binomial distribution. Poisson-type (PT) random measures include the Poisson random measure, negative binomial random measure, and binomial random measure.[3]

References

  1. Kallenberg, Olav (2017). Random Measures, Theory and Applications. Switzerland: Springer. p. 72. doi:10.1007/978-3-319-41598-7. ISBN 978-3-319-41596-3.
  2. Kallenberg, Olav (2017). Random Measures, Theory and Applications. Switzerland: Springer. p. 77. doi:10.1007/978-3-319-41598-7. ISBN 978-3-319-41596-3.
  3. Caleb Bastian, Gregory Rempala. Throwing stones and collecting bones: Looking for Poisson-like random measures, Mathematical Methods in the Applied Sciences, 2020. doi:10.1002/mma.6224