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.
Border-corrected estimator for Ripley K
Definition
Assume the point-process supplier's AC and Countable Choice convention. Let be the stationary locally square-integrable point process with finite positive intensity from Pair-correlation density and Ripley K-function. Given a compact convex observation window with , where is Borel Lebesgue measure as in Lebesgue measurable sets, the family , and the restricted set function , let for Borel be the complete observed counting measure in as in Random measures and locally finite point processes, and fix a radius . Define the border window
where is the closed Euclidean ball under the Euclidean norm used in Pair-correlation density and Ripley K-function. Since is closed, is closed and hence Borel. Let count occurrences, including multiplicities, and put . The border anchor weight is for and otherwise.
If , return not defined. Otherwise because , so . For an occurrence representation , return
Every eligible anchor has its full closed radius- neighborhood inside , so all its radius- partner occurrences are observed. This defines a finite-data procedure only; it asserts no unbiasedness, consistency, or asymptotic guarantee.
Axiom convention. The procedure itself makes no choices; AC and Countable Choice are inherited through its point-process and K dependencies.
Depends on
Used by
Dependency tree · two levels
34 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
- Adrian Baddeley, Spatial Point Processes and their Applications (standard reference, not scraped)
- Günter Last and Mathew Penrose, Lectures on the Poisson Process (2017 author-hosted version) (standard reference, not scraped)