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Smooth functionals extend continuously to the white-noise topology
Statement
Let be the coefficient Hilbert space of Logarithmic negative Sobolev spaces for white noise, with weights . If for some , then the pairing , where , converges absolutely for every and is continuous, with On this extension agrees with the usual inner product .
Facts & Assumptions
Given: The wavelet coefficient weights and the Sobolev coefficient norm in the Statement.
Smooth linear functionals on the white-noise topology: means , and its pairing with an function is the inner product.
Logarithmic negative Sobolev spaces for white noise: , where and the coarsest level has finite multiplicity.
Proof
Since , the level factor is bounded: it tends to zero as , because a positive power of dominates every fixed power of , and the finitely many coarsest levels have finite weights. Thus after increasing a finite constant.
Weighted Cauchy–Schwarz gives . The same bound applied to proves continuity and absolute convergence of the series.
If is the coefficient sequence of , its finite orthonormal projections converge to in because is a complete orthonormal basis. Their inner products with are the finite partial sums of the displayed pairing, and converge to by continuity of the inner product. Step 2.1 identifies the same limit with .
Depends on
Used by
- Linear-functional BvM limit and credible-interval calibration Theorem
- Nonlinear-functional BvM limit under fast L2 contraction Theorem
Cited to discharge well-definedness by Smooth linear functionals on the white-noise topology.
Dependency tree · two levels
5 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
- Castillo and Nickl, Nonparametric Bernstein–von Mises theorems in Gaussian white noise, §1.2 and §2.3.1 (standard reference, not scraped)
- Giné and Nickl, Mathematical Foundations of Infinite-Dimensional Statistical Models, §7.3.4 (standard reference, not scraped)