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.
Uniform laws on finite subset families
Statement
For , let , with . For an integer with , let Then is finite and nonempty, and assigning weight to each member defines a finite probability space on .
Facts & Assumptions
Given: Integers and , the index set , and the subset family in the statement.
The cardinality of a finite set: finiteness means being bijective with a von Neumann natural.
The set of functions between finite sets is finite, with : for finite sets , the function set is finite with cardinality . In particular, the set of maps is finite.
A subset of a finite set is finite, with , and equality holds if and only if : every subset of a finite set is finite.
Finite probability spaces, outcome weights, events, and event probabilities: a finite outcome set with nonnegative weights summing to is a finite probability space, with every subset an event.
The cardinality of a finite set: the cardinality of a finite set is the unique von Neumann natural bijective with it.
The cardinality of a finite set: a finite set has cardinality exactly when it is empty.
Proof
Let . The shift is a bijection from the conventional index set to the von Neumann natural ; for both sets are empty. Thus is finite by [F1]. The set is bijective with , so it is finite as well. For each define its indicator function by if and otherwise. The map is injective because equal indicator functions have equal inverse images of . Its image is a subset of the finite set , which is finite by [F2] and [F3]. Therefore some natural is bijective with this image; composing that bijection with makes bijective with , so [F1] makes it finite.
If , then . If , then because , and the shift is a bijection from to the von Neumann natural ; hence this subset has cardinality by [F1, F5], so . Thus is nonempty. By [F5], its cardinality is defined; by [F6] and nonemptiness, . Give each weight . These weights are nonnegative and their finite sum is .
The outcome set is finite by step 1.1 and its weights satisfy the conditions in step 1.2, so [F4] makes a finite probability space. The argument includes , , and .
Depends on
- The cardinality $\lvert A\rvert$ of a finite set
- Finite probability spaces, outcome weights, events, and event probabilities
- The set $A^{B}$ of functions $B \to A$ between finite sets is finite, with $\lvert A^{B}\rvert = \lvert A\rvert^{\lvert B\rvert}$
- A subset of a finite set is finite, with $\lvert B\rvert \le \lvert A\rvert$, and equality holds if and only if $B = A$
Used by
Dependency tree · two levels
23 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
- P.J. Bickel, F. Götze and W.R. van Zwet, Resampling Fewer Than n Observations, Statistica Sinica 7 (1997), §3, pp. 8–11 (standard reference, not scraped)
- P.B. Stark, Statistics 240 Lecture Notes, Part 8: Resampling Methods, §1.2, PDF pp. 3–7 (standard reference, not scraped)