Poisson Random Measure

Let $(E, \mathcal A, \mu)$ be some measure space with $\sigma$- finite measure $\mu$. The Poisson random measure with intensity measure $\mu$ is a family of random variables $\_$ defined on some probability space $(\Omega, \mathcal F, \mathrm)$ such that

i) $\forall A\in\mathcal,\quad N_A$ is a Poisson random variable with rate $\mu(A)$.

ii) If sets $A_1,A_2,\ldots,A_n\in\mathcal$ don't intersect then the corresponding random variables from i) are mutually independent.

iii) $\forall\omega\in\Omega\;N_(\omega)$ is a measure on $(E, \mathcal )$

Existence

If $\mu\equiv 0$ then $N\equiv 0$ satisfies the conditions i)–iii). Otherwise, in the case of finite measure $\mu$, given $Z$, a Poisson random variable with rate $\mu(E)$, and $X_, X_,\ldots$, mutually independent random variables with distribution $\frac$, define $N_(\omega) = \sum\limits_^ \delta_(\cdot)$ where $\delta_(A)$ is a degenerate measure located in $c$. Then $N$ will be a Poisson random measure. In the case $\mu$ is not finite the measure $N$ can be obtained from the measures constructed above on parts of $E$ where $\mu$ is finite.

Applications

This kind of random measure is often used when describing jumps of stochastic processes, in particular in Lévy–Itō decomposition of the Lévy processes.

Generalizations

The Poisson random measure generalizes to the Poisson-type random measures, where members of the PT family are invariant under restriction to a subspace.

References

* {{cite book , last=Sato , first=K. , year=2010 , title=Lévy Processes and Infinitely Divisible Distributions , publisher=Cambridge University Press , isbn=978-0-521-55302-5

Statistical randomness
Poisson point processes