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]- ^ 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.
- ^ 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.
- ^ 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