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Mecke identity for locally finite Poisson point processes

Statement

Assume AC and Countable Choice as carried by the point-process supplier. Let η be a Poisson point process on Rd with locally finite, sigma-finite intensity measure ν. For every nonnegative function h measurable on the product of the Borel and configuration evaluation sigma-algebras,

E∫h(x,η−δx) η(dx)=∫RdE[h(x,η)] ν(dx).

At each counted occurrence x, η−δx removes exactly one occurrence; coincident remaining occurrences stay. Equivalently, for every such nonnegative f,

E∫f(x,η) η(dx)=∫RdE[f(x,η+δx)] ν(dx).

Both sides are extended nonnegative integrals.

Facts & Assumptions

Given: AC, Countable Choice, a Poisson point process η on Rd with locally finite sigma-finite intensity ν, and nonnegative product-measurable integrands.

[A1]

AC is carried by the transitive random-point-process supplier dependencies; the Mecke calculation below makes no choice. (The Axiom of Choice)

[A2]

Countable Choice is carried by the transitive random-point-process supplier dependencies; the Mecke calculation below makes no choice. (The Axiom of Countable Choice (ACω))

[F1]

A Poisson point process has independent counts on disjoint Borel sets, each with its specified scalar Poisson law; multiplicities are allowed. (Poisson and Cox point processes)

[F2]

A point process is a measurable locally finite counting measure, its evaluation maps generate its sigma-algebra, and occurrences may repeat. (Random measures and locally finite point processes)

[F3]

The scalar Poisson masses are pn=e−λλn/n! and sum to one; in particular npn=λpn−1. (The scalar Poisson count law, Normalization, moments, and generating function of the Poisson count law)

[F4]
[F5]

Adding one occurrence is measurable from its evaluation formula; deleting one counted occurrence is measurable on D={(x,μ):μ({x})≥1}. (Random measures and locally finite point processes, Deleting one occurrence from a locally finite counting measure is measurable)

[F6]

Nonnegative iterated integrals and increasing limits may be interchanged by Tonelli and monotone convergence. (Tonelli's theorem for nonnegative measurable functions on a sigma-finite product, Monotone convergence for the integral)

[F7]

A countable nonnegative weighted sum of measures is a measure. (Nonnegative scalar multiples and countable weighted sums of measures are measures)

Proof

Given: The stated Poisson point process η, intensity measure ν, AC, and Countable Choice.

1.1givenF1F2F3F7construct

First assume λ:=ν(Rd)<∞. If λ=0, then η(Rd)=0 almost surely by [F1], so both identities are zero. If λ>0, put Qν=ν/λ and pn=e−λλn/n!. For each n, let Pn be the law of ∑j=1nδXj when (X1,…,Xn) has product law Qν⊗n, with P0 the point mass at the zero measure. The countable weighted sum P∗=∑n≥0pnPn is a probability measure by the normalized Poisson masses [F3] and the countable weighted-sum result [F7]; evaluation maps of the finite Dirac sum are measurable by [F2].

1.2givenF1F2F4construct

Now let ν be sigma-finite and locally finite. Partition Rd into the explicit bounded Borel cells A1=[−1,1)d and An=[−n,n)d∖[−(n−1),n−1)d for n≥2. Let ηi be the restriction of η to Ai and η−i its restriction to Aic. Each ηi is a finite-intensity Poisson process. The two random configurations ηi and η−i are independent: finite evaluation cylinders refine to disjoint Borel pieces in Ai and Aic, whose counts are independent by [F1], and the generating rectangle pi-system extends this factorization to their evaluation sigma-algebras by [F4].

2.1step 1.1F1F2F3F4algebra

For disjoint Borel sets B1,…,Bm, add their complement as B0 and put qj=Qν(Bj). On the nth component the vector of counts has multinomial mass n!∏j=0mqjkj/kj! when ∑jkj=n. Mixing by pn and summing k0 gives P∗(N(Bj)=kj,1≤j≤m)=∏j=1me−ν(Bj)ν(Bj)kjkj!, using the exponential series; hence P∗ has the defining independent Poisson counts [F1]. Any other Poisson process with finite ν has the same finite-dimensional evaluation laws: refine any finite family of Borel sets into its finitely many disjoint Boolean atoms, apply [F1] to their counts, and recover the original counts by summation. Evaluation cylinders form a generating pi-system for the configuration sigma-algebra [F2], so probability-measure uniqueness [F4] shows that η has law P∗.

3.1step 1.1step 2.1F3F4F6algebra

For any nonnegative product-measurable f, represent η by the mixture law from steps 1.1–1.2. Pathwise the integral against its finite counting measure is a sum over occurrences, so Tonelli [F6] gives E∫f(x,η)η(dx)=∑n≥1pn∫(Rd)n∑i=1nf(xi,∑j=1nδxj) Qν⊗n(dx). Coordinate-permutation invariance of Qν⊗n makes the n summands equal. Reindexing with m=n−1 and npn=λpn−1 changes this to λ∑m≥0pm∫(Rd)m+1f(x1,δx1+∑j=2m+1δxj) Qν⊗(m+1)(dx). Integrating first in x1 and using ν=λQν, the inner mixture over m remaining points is E[f(x,η+δx)] by steps 1.1–1.2. This proves the add-one identity for finite total intensity.

4.1step 3.1step 1.2F4F5F6

Fix i. Independence makes the joint law of (ηi,η−i) the product of their laws, as it agrees with that product on measurable rectangles [F4]. For each fixed background configuration μ, the function fμ(x,ζ)=f(x,ζ+μ) is product-measurable by the addition evaluation formula in [F5]. Apply the finite-intensity add-one identity of step 3.1 to ηi and fμ, then integrate over μ=η−i; this gives E∫f(x,η)ηi(dx)=∫AiE[f(x,η+δx)]ν(dx). Summing over i and using [F6] proves the add-one identity for ν.

5.1step 4.1F5algebra

Let g be the nonnegative integrand in the deletion formula, and put D={(x,μ):μ({x})≥1}. Define H(x,μ)=g(x,μ−δx) on D and H=0 off D; [F5] makes H measurable. Apply the add-one identity of step 4.1 to H. At every occurrence counted by η(dx), (x,η)∈D and H(x,η)=g(x,η−δx); for every x, (x,η+δx)∈D and H(x,η+δx)=g(x,η). This gives the deletion identity. Since η−δx removes one unit mass, all other co-located occurrences remain.

6.1A1A2step 1.1step 2.1step 3.1step 1.2step 4.1step 5.1∎

Steps 1.1–4.1 establish both forms, including the zero-intensity boundary case and multiplicity convention. AC and Countable Choice [A1]–[A2] are inherited supplier assumptions; the local Mecke derivation itself uses no selection, and no DC is used.

Sources

Last and Penrose, Chapter 4, Theorem 4.4 proof and Theorem 4.1 formulation; Baddeley, Lecture 3, Campbell–Mecke formula. The local derivation keeps finite- and sigma-finite intensity cases and multiplicities explicit.

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Sources