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.
Factorial and reduced factorial moment measures are well-defined and locally finite
Statement
Assume AC and Countable Choice as inherited through the random-point-process supplier. Let be a locally square-integrable stationary point process. The pathwise second factorial measure is measurable as a random measure; its expectation and the reduced second factorial moment measure of Second factorial and reduced second factorial moment measures are Borel measures, finite on bounded sets.
Facts & Assumptions
Given: AC, Countable Choice, a random locally finite counting measure on , stationarity, and for every compact .
AC is carried by the transitive point-process supplier dependencies; this proof makes no selection. (The Axiom of Choice)
Countable Choice is carried by the transitive point-process supplier dependencies; this proof makes no selection. (The Axiom of Countable Choice ())
A point process is a measurable random element into locally finite counting measures; each realization is a finite or countable sum of Dirac masses, with repetitions allowed, and evaluations generate the configuration sigma-algebra. (Random measures and locally finite point processes)
For sigma-finite measures the product measure exists and has the rectangle formula. (For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique)
Parameterized nonnegative integration against an evaluation kernel is measurable. (Measure kernel and probability kernel, Measurability of integration against a kernel)
A countable nonnegative sum of measures is a measure. (Nonnegative scalar multiples and countable weighted sums of measures are measures)
Increasing limits pass through nonnegative expectations and integrals; pointwise limits of measurable extended-real functions are measurable. (Monotone convergence for the integral, Expectation of a nonnegative or integrable random variable, Sequential suprema, infima, limsup, liminf, and pointwise limits of measurable functions are measurable)
Local square integrability gives for every compact . (Second factorial and reduced second factorial moment measures)
Proof
Given: The stated locally square-integrable stationary point process and its occurrence-indexed definitions.
Fix a realization . Define directly as a countable sum of Dirac measures; [F4] makes it a measure, and it is finite on compact subsets of because for a compact containing both coordinate projections. On every bounded Borel , this agrees with , where : both terms are finite there and the second removes exactly the equal-index pairs. Thus no subtraction of infinite values is used.
Let . For each Borel , the evaluations and are measurable functions of : the first is obtained by two successive integrations against the evaluation kernel [F3], and the second is evaluation on the Borel preimage under the continuous diagonal map [F1]. Both are finite; their difference is nonnegative and, by step 1.1, equals . These values increase to because increases to all of . Hence [F5] makes measurable.
For pairwise disjoint Borel , countable additivity of each gives pointwise; [F5] passes the increasing partial sums through expectation. Thus is a measure. If is bounded, choose compact with ; then , so [F6] gives .
For Borel , is Borel because is continuous and is Borel. The map is the pushforward under of the restriction of to , so it is a measure; it is measurable in by step 2.1. Taking expectations and using [F5] shows is a measure. If is bounded, is contained in for some compact containing and , so and [F6] makes its expectation finite.
Steps 1.1–4.1 establish the occurrence-indexed factorial measures, their measurability, and local finiteness. Multiplicity is retained because only equal occurrence indices are removed in step 1.1. The axioms [A1]–[A2] are inherited supplier assumptions and are not used by this construction.
Sources
Last and Penrose, Chapter 4, §4.2 and Chapter 8, Proposition 8.7 proof; Baddeley, Lecture 2, §§2.3–2.4. The local proof supplies measurability and handles multiplicities explicitly.
Depends on
- Second factorial and reduced second factorial moment measures
- Stationarity, finite intensity, and spatial ergodicity
- Random measures and locally finite point processes
- Measures on sigma-algebras
- The product sigma-algebra and its finite iterates
- For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique
- Measure kernel and probability kernel
- Measurability of integration against a kernel
- Monotone convergence for the integral
- Nonnegative scalar multiples and countable weighted sums of measures are measures
- Expectation of a nonnegative or integrable random variable
- Sequential suprema, infima, limsup, liminf, and pointwise limits of measurable functions are measurable
- The Axiom of Choice
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
Used by
- A translated lattice exposes recentering, atomic pair measures, and one-occurrence deletion Example
- The reduced second factorial moment measure is the Palm expectation after one-occurrence deletion Theorem
Cited to discharge well-definedness by Second factorial and reduced second factorial moment measures.
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
- Günter Last and Mathew Penrose, Lectures on the Poisson Process (2017 author-hosted version) (standard reference, not scraped)
- Adrian Baddeley, Spatial Point Processes and their Applications (standard reference, not scraped)