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.
The multiscale primitive map is continuous into C[0,1]
Statement
Let be a compactly supported wavelet basis of Logarithmic negative Sobolev spaces for white noise. The Haar basis with its finite scaling coordinate, as specified in Little-o multiscale coefficient space M0(w), is also allowed. Assume each level- wavelet for has zero integral, uniformly bounded overlap, support length at most , and . Let be as in Little-o multiscale coefficient space M0(w), and suppose For define Then this series converges absolutely and uniformly in , , and after adding the finite coarsest-level constant to . If is the coefficient sequence of , then .
Facts & Assumptions
Given: The wavelet geometry, summability condition, and multiscale space in the Statement.
Logarithmic negative Sobolev spaces for white noise: is a complete orthonormal basis of ; finite orthogonal projections converge in .
Proof
For fixed and level , a wavelet whose support lies wholly on one side of contributes zero: it is either disjoint from or its entire support is integrated and its mean is zero. Only supports meeting can contribute. Uniformly bounded overlap bounds their number by a constant, and each coefficient has magnitude at most . Thus . The finitely many coarsest levels have a finite uniform sum as well.
By [F1] and step 1.1, the absolute sum of all terms at levels is at most , uniformly over . Its tails tend uniformly to zero because . Each finite-level sum is continuous in , since is continuous for an function. The uniform limit is continuous. Applying the same estimate to proves the stated operator-norm bound.
If for , the finite orthogonal projections converge to in by [F2]. The map is a continuous functional because . Applying it to the finite projections shows that their coefficient sums converge to , while step 2.1 identifies the same limit with .
Depends on
Used by
Dependency tree · two levels
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Sources
- Castillo and Nickl, On the Bernstein–von Mises phenomenon for nonparametric Bayes procedures, §4.1 (standard reference, not scraped)
- Giné and Nickl, Mathematical Foundations of Infinite-Dimensional Statistical Models, proof of Theorem 7.3.20 (standard reference, not scraped)