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I am trying to understand the definition of point process when reading its Wikipedia article:

Let $S$ be locally compact second countable Hausdorff space equipped with its Borel σ-algebra $B(S)$. Write $\mathfrak{N}$ for the set of locally finite counting measures on $S$ and $\mathcal{N}$ for the smallest σ-algebra on $\mathfrak{N}$ that renders all the point counts

$ \Phi_B : \mathfrak{N} \to \mathbb{Z}_{+}, \varrho \mapsto \varrho(B)$

for relatively compact sets $B$ in $B$-measurable.

A point process on $S$ is a measurable map $ \xi: \Omega \to \mathfrak{N} $ from a probability space $(\Omega, \mathcal F, P)$ to the measurable space $(\mathfrak{N},\mathcal{N})$.

My questions are:

  1. Is the counting measure the one that gives the cardinality of a measurable subset, as defined in its Wikipedia article? If yes, isn't it that there is only one counting measure on a measurable space, and why in the definition of point process, does "write $\mathfrak{N}$ for the set of locally finite counting measures on $S$" imply that there are more than one counting measures on $S$?
  2. It has been noted[citation needed] that the term point process is not a very good one if S is not a subset of the real line, as it might suggest that ξ is a stochastic process.

    Is a point process a stochastic process?

    If no, when can it be? How are the two related?

Thanks and regards!

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(1) Every locally finite counting measure on $S$ is of the form $\sum_{x\in \Lambda}\delta_x$ where $\Lambda$ is a locally finite subset of $S$. That is, $\Lambda$ should intersect every compact set only finitely often. Of course, there are infinitely many choices of subset $\Lambda$, and thus plenty of counting measures.

(2) In the case where $S=[0,\infty)$ we can define an integer valued stochastic process by setting $X(t)=\xi[0,t]$. That is, $X(t)$ is the amount of mass that the (random) measure $\xi$ assigns to the set $[0,t]$. For instance, the Poisson process can be expressed in this way. But for a general point process, there may be no notion of a "time parameter" and so it is not thought of as a stochastic process.

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    Thanks! (1) Is a point process a stochastic process, if and only if the locally finite counting measure on$S$all have distribution functions? (2) For a point process $\xi$, is $\xi(A), \forall A \in B(S)$ always a measurable mapping, i.e. random variable? (3) Are there any references regarding my two questions? Thanks!2011-04-28