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.
Conditional integration through a regular conditional law
Statement
Let be a specified regular conditional distribution of given . For measurable , the measurable function satisfies
This assertion is choice-free. Under AC, it says as nonnegative almost-sure classes. If is measurable and , put . Then , , and the signed integral on , extended by zero off , is a real integrable conditional-expectation version of . Its identification with the AC-based conditional-expectation class uses AC only for that class convention.
Facts & Assumptions
Given: The hypotheses and conventions in the statement.
Kernel event evaluations satisfy every conditioning-event identity. Regular conditional distribution.
A probability kernel integrates nonnegative measurable functions measurably and gives measurable zero-filled signed integrals on the absolute-integrability set. Measurability of integration against a kernel.
Increasing nonnegative limits pass through every section integral and every event integral. Monotone convergence for the integral.
Nonnegative measurable functions have an explicit increasing simple approximation. Every nonnegative measurable function is the increasing limit of simple measurable functions.
Under AC nonnegative conditional classes are characterized by all conditional event identities. Conditional monotone convergence.
The real integrable version is unique up to almost-sure equality. Conditional expectation is unique almost surely.
AC is used only for the inherited conditional-class existence convention (RN selections and nonnegative truncations). The Axiom of Choice.
For real |f|, the absolute expectation also equals its integral against the law of X. Change of variables for expectation.
Proof
For , the assertion is [F1]. If is nonnegative simple with disjoint measurable and finite coefficients, then , so finite additivity of integrals proves the identity by summing the indicator identities. It includes the empty sum, giving , and , giving .
For general nonnegative , choose the prescribed simple approximants of [F4]. For every , [F3] for the probability gives . The function is product-measurable since inverse images are ; thus [F2] ensures -measurability of . Applying [F3] on each on both sides of step 1.1 proves the displayed identity, allowing infinity. No representative selection or AC occurred. If using conditional-class notation, [F5] identifies this characterized nonnegative function with the class, under [F7].
For real with , step 2.1 at and gives . Also by [F8], checking the equivalent law integrability condition. The set is measurable. For every positive integer , , hence . On , both are finite and their difference is the signed integral. Fill by zero off ; it is real measurable by [F2], and on , so is integrable. The two nonnegative identities from step 2.1 have finite integrals, at most , so subtraction gives . Removing changes neither integral, since it is measurable null. Thus is a conditional version; [F6] gives its almost-sure uniqueness, and [F7] supplies only the inherited class notation. No infinity is subtracted from infinity.
Depends on
- Regular conditional distribution
- Measurability of integration against a kernel
- Monotone convergence for the integral
- Every nonnegative measurable function is the increasing limit of simple measurable functions
- Conditional monotone convergence
- Conditional expectation is unique almost surely
- The Axiom of Choice
- Change of variables for expectation
Used by
Dependency tree · two levels
35 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)