Alphabeta Math
AlgorithmAlgorithm: Literature-sourcedProof: Not applicablePipeline-generatedjudge pass (gpt-6-luna)
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 η 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 W⊆Rd with 0<∣W∣<∞, where ∣⋅∣ is Borel Lebesgue measure as in Lebesgue measurable sets, the family L(Rn), and the restricted set function λn, let ξ(A):=η(A∩W) for Borel A be the complete observed counting measure in W as in Random measures and locally finite point processes, and fix a radius r≥0. Define the border window

W−r:={x∈W:B‾[x,r]⊆W},

where B‾[x,r] is the closed Euclidean ball under the Euclidean norm used in Pair-correlation density and Ripley K-function. Since W is closed, W−r=⋂∥z∥2≤r(W−z) is closed and hence Borel. Let Nξ(A)=ξ(A) count occurrences, including multiplicities, and put ρ^=Nξ(W)/∣W∣. The border anchor weight is wW,r(x)=1 for x∈W−r and 0 otherwise.

If Nξ(W−r)=0, return not defined. Otherwise Nξ(W)>0 because W−r⊆W, so ρ^Nξ(W−r)>0. For an occurrence representation ξ=∑iδXi, return

K^border(r):=1ρ^ Nξ(W−r)∑i:Xi∈W−r∑j≠i1{∥Xj−Xi∥2≤r}.

Every eligible anchor has its full closed radius-r neighborhood inside W, so all its radius-r 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