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.
Independent polynomial-chaos tensor products are orthonormal
Statement
Let and form from Finite orthonormal polynomial tensor basis for indices in the admissible degree box. Then each belongs to and In particular, the family indexed by any finite subset of the box is orthonormal; when , it includes the constant mode .
Facts & Assumptions
Given: Independent real inputs with product law , admissible one-dimensional moment data, and indices in the finite degree box.
Independent random elements have product joint law identifies the joint law with .
Tonelli and Fubini hold for the uncompleted product of sigma-finite measure spaces (Tonelli's theorem for nonnegative measurable functions on a sigma-finite product, Fubini's theorem for L^1 functions on a sigma-finite product); finite iteration gives factorization for a finite product.
The product law on is the full-mass restriction specified in Finite orthonormal polynomial tensor basis, so Tonelli and Fubini apply to the product of the restricted marginal laws.
Proof
For in the admissible box set . Each is integrable because it is a polynomial of degree at most and the moment is finite. By [F4], the restricted measure on is the product of the restricted marginal laws. Repeated Tonelli [F3] first gives ; repeated Fubini [F3] then gives . The product-law statement [F2] identifies the ambient finite product measure with the joint law, and [F4] restricts it to .
The factorization in step 1.1 and univariate orthonormality [F1] give . This product is one exactly when every coordinate agrees, and zero otherwise; with it also shows . Multiplying over the finitely many coordinates gives , so whenever the finite family includes this constant mode.
Depends on
- Finite orthonormal polynomial tensor basis
- Positive moment Gram matrices produce orthonormal polynomial families
- Tonelli's theorem for nonnegative measurable functions on a sigma-finite product
- Fubini's theorem for L^1 functions on a sigma-finite product
- Independent random elements have product joint law
Used by
- Conditional spatial finite-element rate for stochastic Galerkin Corollary
- Tensor Gaussian rules recover polynomial-chaos coefficients exactly Corollary
- Christoffel sampling is normalized and its weighted Gram has mean identity Lemma
- Finite chaos projection, Parseval identity, mean, and variance Theorem
- Projection and quadrature errors split in L2 Theorem
- Stochastic Galerkin coercivity and Cea quasi-optimality Theorem
- Tensor products are dense in Bochner L2 under scalar and spatial density Theorem
- Weighted least squares is stable on the good Gram event Theorem
Cited to discharge well-definedness by Finite orthonormal polynomial tensor basis.
Dependency tree · two levels
27 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
- Dongbin Xiu, Fast Numerical Methods for Stochastic Computations, §3.2 (standard reference, not scraped)
- Bruno Sudret, Polynomial Chaos Expansions and Stochastic Finite Element Methods, §3.2.2 (standard reference, not scraped)