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.
Regular conditional law for a finite partition
Example
Let be a finite measurable partition of a probability space, let be measurable, and supply a fixed target probability . For put
This is a regular conditional law of X given . For example, on take point masses , cells , and . With , the conditional laws on the three cells are respectively , , and .
Facts & Assumptions
Given: The hypotheses and conventions in the example.
RCDs are probability kernels satisfying all conditioning-event identities. Regular conditional distribution.
A probability kernel has pointwise probability sections and measurable evaluations. Measure kernel and probability kernel.
Verification
For a positive-mass cell, preimages under X preserve disjoint unions, so is countably additive, vanishes at the empty set and has total mass . Division by this positive finite mass gives a probability. On zero-mass cells the supplied is a probability. For each A the evaluation is constant on every cell and is therefore measurable for the finite partition sigma-algebra. Every event H in that sigma-algebra is a union of cells: the set of such unions is itself a sigma-algebra containing the cells. Thus Each zero cell contributes zero on both sides, and an empty cell can be ignored. This proves [F1]–[F2].
In the displayed finite model the cell masses are . On the first cell, and , so the conditional probabilities are and . On the second cell, gives probability one at 1. The third uses the specified filler despite , since its entire cell has mass zero. For instance testing A={1} and H=Omega gives ; testing H= gives on both sides. Hence the calculated kernels have exactly the claimed values.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
5 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)