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.
Finite-window spatial observations and configurations
Definition
Fix an integer and a bounded Borel set . Under Countable Choice every Borel subset of is Lebesgue measurable (Assuming countable choice, every Borel subset of is Lebesgue measurable), so Lebesgue measure is defined on each such set. Write for Borel , and for the trace Borel sigma-algebra on . Every member of is Borel in because is Borel. Define by . If is pairwise disjoint in , then each and their union are Borel in , so countable additivity of gives and . Thus is a measure on the trace sigma- algebra. Since is bounded, (Lebesgue measure is sigma-finite, and every metrically bounded subset of has finite outer measure). The only choice axiom used to regard as a measure in this library is Countable Choice, as recorded in Assuming countable choice, is a sigma-algebra containing every elementary set and is a complete measure extending elementary volume.
The finite configuration space on , denoted , is the measurable subspace of locally finite counting measures on consisting of those with and . It is measurable in the ambient evaluation sigma-algebra because both and are evaluation maps for Borel sets. Thus each has a representation for a finite and ; repeated locations are allowed and represent multiplicity. Its sigma-algebra is the trace of the evaluation sigma-algebra: it is generated by for Borel .
For a finite ambient configuration , its observation in is , where . The observation includes precisely the locations belonging to : a boundary location is included when and only when it belongs to . This convention does not replace by its interior or its closure.
Remarks
This definition allows multiplicities and makes no simplicity claim. The separate finite-intensity Poisson simplicity argument is in A finite Poisson process with atomless intensity is simple.
Depends on
- Random measures and locally finite point processes
- The Borel sigma-algebra of a topological space
- Measurable spaces and measurable sets
- Sigma-algebras
- The sigma-algebra generated by a family of sets
- Nonempty intersections of sigma-algebras are sigma-algebras, so the generated sigma-algebra exists and is minimal
- Measures on sigma-algebras
- The Dirac set function at a point
- A Dirac set function is a probability measure
- Nonnegative scalar multiples and countable weighted sums of measures are measures
- Restriction of a measure to a measurable set
- The restriction of a measure to a measurable set is a measure
- Lebesgue measurable sets, the family $\mathcal{L}(\mathbb{R}^n)$, and the restricted set function $\lambda_n$
- Assuming countable choice, $\mathcal{L}(\mathbb{R}^n)$ is a sigma-algebra containing every elementary set and $\lambda_n$ is a complete measure extending elementary volume
- Lebesgue measure is sigma-finite, and every metrically bounded subset of $\mathbb{R}^n$ has finite outer measure
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- Assuming countable choice, every Borel subset of $\mathbb{R}^n$ is Lebesgue measurable
Used by
- Finite-window Gibbs density relative to a Poisson reference Definition
- Finite-window point-process pseudolikelihood Definition
- Papangelou conditional intensity on a finite window Definition
- Exact and pseudo likelihoods for finite-window Poisson and Strauss models Example
- Homogeneous Poisson rate estimation at positive-count and zero-count boundaries Example
- A positive finite-window Gibbs density has its density-ratio Papangelou intensity Lemma
- Finite products of measures with densities have the product density Lemma
- Finite-window pair counts are measurable Lemma
- Products of a measurable intensity over finite configurations are measurable Lemma
- Homogeneous Poisson rate MLE in a positive-volume window Proposition
- Finite-window spatial Poisson likelihood relative to unit rate Theorem
Dependency tree · two levels
65 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, complete lecture notes (standard reference, not scraped)
- Günter Last and Mathew Penrose, Lectures on the Poisson Process, author-hosted 2017 version (standard reference, not scraped)