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.
Covering, packing, bracketing numbers, and metric entropy
Definition
A pseudometric on a set is a nonnegative symmetric function with and ; distinct points may have distance zero. For a nonempty subset and , the covering number is the least finite for which there are centers such that every satisfies for some . It is if no finite cover exists. Set .
The packing number is the largest size of a finite subset whose distinct members have pairwise distance strictly greater than . If the sizes of such finite subsets are unbounded, put ; put . These conventions give the radius comparison proved in Packing and covering numbers with explicit radius conventions ↗. The metric entropy is for nonempty classes, with .
For a probability measure on and a measurable real function , set for when the integral is finite, using the real-power convention, including , of Real powers for positive bases, with the zero-base positive-exponent convention. If the integral is , define directly. The function is measurable by Arithmetic and lattice operations preserve measurability whenever they are defined, and Positive real powers preserve measurability makes measurable, so this nonnegative extended integral is well-defined. On a class of functions with finite such norms, is a pseudometric: nonnegativity, symmetry, and zero diagonal follow from the absolute value, and the triangle inequality is Minkowski's inequality for integrals, including . Functions that agree -almost everywhere have distance zero. We write for this covering number.
For measurable real endpoints -almost everywhere, the bracket is the set of measurable functions satisfying -almost everywhere. Its width is . The bracketing number is the least number of such brackets of width at most that cover , or if no finite cover exists; for the empty class set it equal to . Centers of metric covers are in ; bracket endpoints need not be.
Depends on
- Probability measures and probability spaces
- Real powers for positive bases, with the zero-base positive-exponent convention
- Positive real powers preserve measurability
- Arithmetic and lattice operations preserve measurability whenever they are defined
- Minkowski's inequality for integrals, including $p = \infty$
- The exponent, product, quotient, and iterated-power laws for positive real bases and real exponents
Used by
- Finite thresholds rule out an r-independent VC covering bound Counterexample
- Donsker classes and asymptotic equicontinuity Definition
- The uniform empirical CDF converges to a Brownian bridge Example
- The uniform threshold class is Glivenko–Cantelli Example
- Bracketing entropy implies empirical-process asymptotic equicontinuity Lemma
- Countably partitioned sieves yield posterior contraction by weighted entropy Lemma
- Integrated and conditional uniform convergence of Cox risk-set sums Lemma
- Logarithmic L-r covering bound for VC classes of sets Lemma
- Packing and covering numbers with explicit radius conventions Lemma
- Uniform convergence of empirical risk-set fractions Lemma
- Dudley entropy-integral bound for sub-Gaussian processes Theorem
- Functional central limit theorem by bracketing entropy Theorem
- Gaussian concentration-function sieve bounds Theorem
- Glivenko–Cantelli theorem by bracketing entropy Theorem
- VC-subgraph covering entropy with its finite-r logarithmic bound Theorem
Dependency tree · two levels
33 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
- David Pollard, Empirical Processes: Theory and Applications, Chapter 3, Definition 3.3 (standard reference, not scraped)
- Jon A. Wellner, Empirical Processes: Theory and Applications, Chapter 1, Sections 5–6 (standard reference, not scraped)