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.
A probability measure on the line has no nonzero translation period
Statement
Let be a Borel probability measure on . If a real number satisfies for every Borel set , then .
Statement references
Facts & Assumptions
Given: A Borel probability measure on and an such that for every Borel set .
A probability measure is countably additive and has total mass one. Probability measures and probability spaces
is the translate of . Translation of a subset of
The real line is a complete ordered field, hence its positive reals satisfy the Archimedean property. Dedekind-complete ordered real field, Every complete ordered field is Archimedean
Proof
Given: The objects and hypotheses in the Statement.
Suppose and put . Invariance under translation by implies invariance under translation by : when , apply the given identity to . The half-open intervals , , partition and are translates of , so they all have the same measure .
If , choose a natural by [F3]. The first intervals are disjoint, so finite additivity gives , a contradiction. Hence .
Countable additivity over the partition now gives , again a contradiction. Therefore .
References
Depends on
Used by
Dependency tree · two levels
11 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
- Jonas Peters, Joris Mooij, Dominik Janzing, and Bernhard Schölkopf, Causal Discovery with Continuous Additive Noise Models, Appendix A.5 (standard reference, not scraped)