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.
Strong distribution convergence implies weak convergence
Statement
If a net of distributions converges strongly, it converges weakly to the same distribution. In particular this holds for sequences. The implication requires no choice axiom.
Facts & Assumptions
Strong convergence is convergence uniformly on every bounded test set; weak convergence is pointwise convergence on tests, and every singleton test set is bounded (Weak and strong topologies on distributions).
Proof
Given: strongly.
Fix a test . By F1 the singleton is bounded and .
The test was arbitrary, so F1 identifies these scalar limits as weak convergence. This uses one given test at a time, without a simultaneous selection. For the zero test the seminorm is zero; on the empty domain the sole distribution is zero. No converse for arbitrary nets is asserted.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
3 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
- Semyon Dyatlov, Lecture notes for 18.155 (2022) (standard reference, not scraped)