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.
Mecke identity for locally finite Poisson point processes
Statement
Assume AC and Countable Choice as carried by the point-process supplier. Let be a Poisson point process on with locally finite, sigma-finite intensity measure . For every nonnegative function measurable on the product of the Borel and configuration evaluation sigma-algebras,
At each counted occurrence , removes exactly one occurrence; coincident remaining occurrences stay. Equivalently, for every such nonnegative ,
Both sides are extended nonnegative integrals.
Facts & Assumptions
Given: AC, Countable Choice, a Poisson point process on with locally finite sigma-finite intensity , and nonnegative product-measurable integrands.
AC is carried by the transitive random-point-process supplier dependencies; the Mecke calculation below makes no choice. (The Axiom of Choice)
Countable Choice is carried by the transitive random-point-process supplier dependencies; the Mecke calculation below makes no choice. (The Axiom of Countable Choice ())
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)
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)
The scalar Poisson masses are and sum to one; in particular . (The scalar Poisson count law, Normalization, moments, and generating function of the Poisson count law)
Finite product measures exist and have the rectangle formula; the product law of independent random elements is determined on the generating rectangle pi-system. (For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique, Measures agreeing on a generating pi-system are equal under an increasing finite-measure exhaustion from that pi-system, Dynkin's pi-lambda theorem)
Adding one occurrence is measurable from its evaluation formula; deleting one counted occurrence is measurable on . (Random measures and locally finite point processes, Deleting one occurrence from a locally finite counting measure is measurable)
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)
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 , intensity measure , AC, and Countable Choice.
First assume . If , then almost surely by [F1], so both identities are zero. If , put and . For each , let be the law of when has product law , with the point mass at the zero measure. The countable weighted sum 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].
Now let be sigma-finite and locally finite. Partition into the explicit bounded Borel cells and for . Let be the restriction of to and its restriction to . Each is a finite-intensity Poisson process. The two random configurations and are independent: finite evaluation cylinders refine to disjoint Borel pieces in and , whose counts are independent by [F1], and the generating rectangle pi-system extends this factorization to their evaluation sigma-algebras by [F4].
For disjoint Borel sets , add their complement as and put . On the th component the vector of counts has multinomial mass when . Mixing by and summing gives using the exponential series; hence has the defining independent Poisson counts [F1]. Any other Poisson process with finite 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 has law .
For any nonnegative product-measurable , represent 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 Coordinate-permutation invariance of makes the summands equal. Reindexing with and changes this to Integrating first in and using , the inner mixture over remaining points is by steps 1.1–1.2. This proves the add-one identity for finite total intensity.
Fix . Independence makes the joint law of the product of their laws, as it agrees with that product on measurable rectangles [F4]. For each fixed background configuration , the function is product-measurable by the addition evaluation formula in [F5]. Apply the finite-intensity add-one identity of step 3.1 to and , then integrate over ; this gives Summing over and using [F6] proves the add-one identity for .
Let be the nonnegative integrand in the deletion formula, and put . Define on and off ; [F5] makes measurable. Apply the add-one identity of step 4.1 to . At every occurrence counted by , and ; for every , and . This gives the deletion identity. Since removes one unit mass, all other co-located occurrences remain.
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.
Depends on
- The Axiom of Choice
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- The Dirac set function at a point
- Law or distribution of a random element
- Measures on sigma-algebras
- Measure kernel and probability kernel
- Series in the nonnegative extended real line
- Poisson and Cox point processes
- The product sigma-algebra and its finite iterates
- Random measures and locally finite point processes
- The scalar Poisson count law
- Deleting one occurrence from a locally finite counting measure is measurable
- The law of a random element is a probability measure
- Normalization, moments, and generating function of the Poisson count law
- A Dirac set function is a probability measure
- Dynkin's pi-lambda theorem
- Measurability of integration against a kernel
- Measures agreeing on a generating pi-system are equal under an increasing finite-measure exhaustion from that pi-system
- Monotone convergence for the integral
- Nonnegative scalar multiples and countable weighted sums of measures are measures
- For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique
- Tonelli's theorem for nonnegative measurable functions on a sigma-finite product
Used by
Dependency tree · two levels
81 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)