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 Dirac probability measure concentrates all mass at one point
Example
Let . The Dirac probability measure satisfies exactly when and otherwise has value . For measurable , its same-ambient restriction satisfies
Facts & Assumptions
Given: A measurable space , a point , and a measurable set .
The Dirac set function at is a probability measure and has value exactly on sets containing (A Dirac set function is a probability measure).
Restriction to is the measure on the original sigma-algebra (The restriction of a measure to a measurable set is a measure).
Verification
For every measurable , [L1] gives if and otherwise; in particular its values on and are and .
By [L2], . If , this equals exactly when , so it is ; if , it is always .
Steps 1.1 and 1.2 verify the concentration and both restriction cases, including and .
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
7 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
- S. Axler, Measure, Integration & Real Analysis, Example 2.55 (standard reference, not scraped)