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.
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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
The normalized Matérn kernel in the 2π Fourier convention
Definition
Fix the dimension of an admitted domain, a smoothness parameter , and an inverse length . Assume Countable Choice for the Lebesgue-integration suppliers used to justify this formula; this definition does not assume full Choice. Put and All integrals are Lebesgue integrals with respect to (Lebesgue measurable sets, the family , and the restricted set function ), and is the Euler integral of The real Gamma function by Euler's integral. With the negative-sign Fourier transform convention , define the normalized stationary kernel by The corresponding length parameter in the usual Matérn notation is . For , define the modified Bessel function in its integral normalization by The classical Bessel expression for when , its value , absolute convergence, continuity, and radial symmetry are established by The Matérn spectral integral, normalization, and radial formula ↗. This definition makes no finite-precision stability claim.
Depends on
Used by
- A small fill distance can coexist with poor separation and conditioning Example
- A smooth scattered surface inherits the Matérn fill-distance rate Example
- Separated Matérn sites have a positive Gram lower bound Lemma
- The Matérn native space has its weighted Fourier norm and strict Gram positivity Lemma
- The Matérn spectral integral, normalization, and radial formula Lemma
- The restricted Matérn native space is the Sobolev quotient space Lemma
- Matérn interpolation and its power function converge at rate h^ν Theorem
- Native Matérn interpolation is an orthogonal projection with power function Theorem
Dependency tree · two levels
14 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
- Rasmussen and Williams, Gaussian Processes for Machine Learning, Chapter 4, equations (4.14)–(4.15) (standard reference, not scraped)
- Gregory E. Fasshauer, MATH 590: Meshfree Methods, Chapter 13, pp. 34–36 (standard reference, not scraped)