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.
Completeness of normalized probabilists’ Hermite polynomials in Gaussian L²
Statement
Assume AC. Let be the standard Gaussian probability measure on with density , let be the monic probabilists’ Hermite polynomial defined by , , and , and put . Then is a complete orthonormal basis of real . Every has the norm-convergent expansion , where , and . If is continuous and nonpolynomial, then for an unbounded set of indices . Every globally Lipschitz real function belongs to , so this conclusion applies to every globally Lipschitz nonpolynomial activation.
Statement references
Facts & Assumptions
Given: AC; the standard Gaussian measure ; the monic probabilists’ Hermite polynomials and their normalized versions .
AC is the axiom that every family of nonempty sets has a choice function (The Axiom of Choice). It supplies countable choice for the Fourier uniqueness and completeness interfaces (AC supplies countable selections and prescribed serial paths).
The standard Gaussian measure has Lebesgue density (Standard normal and normal laws).
The are monic of degree , orthogonal under with , and span the polynomials degree by degree (The monic probabilists' Hermite polynomials, Gaussian orthogonality and the monomial expansion of the Hermite polynomials).
Complex is complete under countable choice, and is dense in Lebesgue (Complex completeness, density, and inner product: the consumer interface).
With the convention , the Fourier transform is well-defined and is unique: equal transforms of two functions imply equality almost everywhere (Fourier transform on complex L1 classes, The integral transform is representative independent, Uniqueness of the L1 Fourier transform).
The complex exponential is its everywhere-convergent power series (The complex exponential by its power series, The complex exponential series converges absolutely for every complex argument).
For square-integrable real random variables, (Cauchy-Schwarz for random variables).
Pointwise convergence under one integrable majorant permits passage of integrals to the limit (Dominated convergence).
A standard normal variable has finite second moment (A normal variable has finite fourth moment).
Proof
Let be orthogonal to every . Each monomial is a finite linear combination of by [F2], and each fixed polynomial is square-integrable under because a polynomial times has an integrable tail. Therefore for every .
Put , so by [F1] and [F6]. For every , Cauchy–Schwarz and completion of the square give , since each half-line integral is at most .
Fix . The partial sums converge pointwise to and satisfy by [F5]. Applying [F7] with the integrable majorant from step 1.2 gives , because every term is a scalar multiple of a zero moment from step 1.1.
By [F4], implies Lebesgue-almost everywhere. Since for all , -almost everywhere. Thus the only function orthogonal to all the is zero.
For arbitrary real , set and . Orthogonality in [F2] gives and, for , ; hence the partial sums are Cauchy. View them in complex ; by [F3] and [A1] they converge there, and their limit is real because every is real. The residual is orthogonal to each , hence zero by step 3.1. Therefore in and taking norms yields Parseval’s identity.
If only finitely many are nonzero, step 4.1 makes equal almost everywhere to a polynomial . If is continuous and differs from at some point, it differs on a nonempty open interval, which has positive measure by [F1]; hence everywhere, contrary to nonpolynomiality. Thus a continuous nonpolynomial has unbounded coefficient support. A globally Lipschitz obeys ; [F8] gives , so this growth bound implies .
Depends on
- Cauchy-Schwarz for random variables
- Uniqueness of the L1 Fourier transform
- The Axiom of Choice
- The complex exponential by its power series
- Fourier transform on complex L1 classes
- The monic probabilists' Hermite polynomials
- Standard normal and normal laws
- AC supplies countable selections and prescribed serial paths
- Complex completeness, density, and inner product: the consumer interface
- The complex exponential series converges absolutely for every complex argument
- Gaussian orthogonality and the monomial expansion of the Hermite polynomials
- The integral transform is representative independent
- A normal variable has finite fourth moment
- Dominated convergence
Used by
Dependency tree · two levels
71 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
- Arthur Jacot, Franck Gabriel, and Clément Hongler, Neural Tangent Kernel: Convergence and Generalization in Neural Networks (standard reference, not scraped)
- MIT OpenCourseWare 18.102, Introduction to Functional Analysis, Lecture 24 (standard reference, not scraped)