Mixed binomial process

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

Definition edit

Let   be a probability distribution and let   be i.i.d. random variables with distribution  . Let   be a random variable taking a.s. (almost surely) values in  . Assume that   are independent and let   denote the Dirac measure on the point  .

Then a random measure   is called a mixed binomial process iff it has a representation as

 

This is equivalent to   conditionally on   being a binomial process based on   and  .[1]

Properties edit

Laplace transform edit

Conditional on  , a mixed Binomial processe has the Laplace transform

 

for any positive, measurable function  .

Restriction to bounded sets edit

For a point process   and a bounded measurable set   define the restriction of  on   as

 .

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

 

and some random variable  .

Also if   is a Poisson process or a mixed Poisson process, then   is a mixed binomial process.[2]

Examples edit

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 edit

  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