Alphabeta Math
TheoremStatement: Literature-sourcedProof: AI-adaptedPipeline-generated
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Existence and uniqueness of the full Palm law and the refined Campbell identity

Statement

Assume AC and Countable Choice as carried by the point-process supplier. For every stationary locally finite point process η on Rd with finite positive intensity ρ, there is a unique probability law P0 on the configuration space satisfying

E∫h(x,θxη) η(dx)=ρ∫Rd∫h(x,μ) P0(dμ) dx

for every nonnegative product-measurable h. Moreover, P0-almost every configuration has at least one occurrence at the origin.

Facts & Assumptions

Given: AC, Countable Choice, a stationary locally finite point process η with finite positive intensity ρ, and its configuration space.

[A1]

AC is carried by the transitive random-point-process supplier dependencies; the local Palm construction uses no selection. (The Axiom of Choice)

[A2]

Countable Choice is carried by the transitive random-point-process and Lebesgue-measure supplier dependencies. (The Axiom of Countable Choice (ACω))

[F1]

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

[F2]

Stationarity is invariance under the stated recentering action; its intensity mean measure is ρλd. (Stationarity, finite intensity, and spatial ergodicity, A stationary point process of finite intensity has mean measure rho times Lebesgue measure)

[F3]

The recentering action (x,μ)↦θxμ is jointly measurable. (Recentering a point configuration at a spatial location, The spatial recentering action is jointly measurable)

[F4]

Every locally finite translation-invariant Borel measure on Rd is its unit-cube mass times Lebesgue measure. (A locally finite translation-invariant Borel measure on Rd is a multiple of Lebesgue measure)

[F5]

Parameterized nonnegative integration against the evaluation kernel is measurable, and increasing limits pass through expectations. (Measure kernel and probability kernel, Measurability of integration against a kernel, Monotone convergence for the integral)

Proof

Given: AC, Countable Choice, a stationary point process η with 0<ρ=Eη(Q)<∞, Q=[0,1)d, and the recentering action.

1.1F1F2F3F5

For each configuration event A and Borel set B⊆Rd, define CA(B):=E∫1B(x)1A(θxη) η(dx). The integrand is measurable by [F3], so this is defined. For disjoint Bj, the indicators of their finite unions increase to the indicator of their union; monotone convergence [F5] and countable additivity of each η give CA(⋃jBj)=∑jCA(Bj). Also 0≤CA(B)≤Eη(B)=ρλd(B) by [F2], so CA is locally finite.

2.1givenF2F3step 1.1

For any y∈Rd, recentering has the flow property θx+y=θxθy. Changing the location variable in the integral gives CA(B+y)=E∫1B(u)1A(θu(θyη)) (θyη)(du)=CA(B), where the last equality uses stationarity of η. Thus CA is translation invariant.

3.1A2step 1.1step 2.1F4F5

By [F4] there is a constant cA=CA(Q) such that CA(B)=cAλd(B) for every Borel B. For disjoint configuration events Aj, monotone convergence in the indicator sum gives c∪jAj=∑jcAj. Also cN=Eη(Q)=ρ and c∅=0. Hence P0(A):=cA/ρ is a probability measure on the configuration sigma-algebra.

4.1step 3.1F3F5F6

For Borel B and configuration event A, the definition gives E∫1B(x)1A(θxη) η(dx)=CA(B)=ρλd(B)P0(A). Define the Campbell measure Π(E)=E∫1E(x,θxη)η(dx) on the product space. Joint measurability [F3] and monotone convergence [F5] make Π a measure. It is sigma-finite since Π(Qn×N)=Eη(Qn)=ρ∣Qn∣<∞ for the increasing bounded cubes Qn=[−n,n)d. The product measure ρ(λd⊗P0) is sigma-finite by [F6] and agrees with Π on rectangles by the displayed identity; uniqueness on the generating rectangle pi-system therefore gives equality of these measures. Integrating any nonnegative product-measurable h against them proves the refined Campbell identity.

5.1step 3.1step 4.1algebra

If P10 is another probability law satisfying the refined identity, use h(x,μ)=1Q(x)1A(μ) to obtain ρP10(A)=CA(Q)=ρP0(A) for every configuration event A; since ρ>0, P10=P0.

5.2step 4.1F3algebra

Let A0={μ:μ({0})=0}. For every counted occurrence x of η, the recentered configuration θxη has an occurrence at 0, so the left side of the refined identity with h(x,μ)=1Q(x)1A0(μ) is zero. Its right side is ρP0(A0) because ∣Q∣=1. Hence P0(A0)=0: the full Palm law retains at least the distinguished occurrence at the origin, even when other occurrences coincide there.

6.1A1A2step 4.1step 5.1step 5.2∎

The constructed law satisfies the defining identity by step 4.1, is unique by step 5.1, and has the origin-atom property by step 5.2. AC and Countable Choice [A1]–[A2] are inherited supplier assumptions; the construction itself uses no choice beyond the Lebesgue-measure supplier.

Sources

Last and Penrose, Chapter 9, Theorem 9.1 and Lemma 9.2; Baddeley, Lecture 3, §3.2. The construction is given locally rather than by conditioning on a null event.

Depends on

Used by

Cited to discharge well-definedness by The full Palm distribution defined by the refined Campbell identity.

Dependency tree · two levels

67 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources