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.
A deterministic kernel from a measurable map
Example
A measurable map defines the deterministic probability kernel . For another measurable map , composition of kernels satisfies pointwise.
Facts & Assumptions
Given: The hypotheses and conventions in the example.
Kernel sections are pointwise measures and event evaluations are measurable. Measure kernel and probability kernel.
Kernel composition is defined by integrating the second evaluation against the first. Composition of probability kernels.
The composition candidate is a probability kernel. Kernel composition is well defined and associative.
Verification
For fixed s, vanishes on the empty set and equals one on T. If are disjoint, at most one contains g(s), so . This is countable additivity, so the section is the Dirac probability at g(s). For a measurable A, the evaluation is ; its set is measurable by hypothesis. These are exactly [F1].
By [F2] and [F3], for a measurable , The middle equality is the integral of an indicator of the measurable set . The composite h after g is measurable because . This proves the formula at every s, with no reference to a null set. For example on real Borel spaces take and . Then the composite section is ; at s=2 its mass on (4,6) is one and on (0,4] is zero. If S is empty the assertions are vacuous; an empty T with nonempty S cannot support the assumed map g.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
8 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
- Durrett, Probability: Theory and Examples, fifth edition (standard reference, not scraped)
- Varadhan, Probability Theory, Chapter 4 (standard reference, not scraped)