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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 density ratio is undefined on zero marginal fibres
Statement refuted
False assertion: a joint probability density always defines its conditional density by the ratio at every y.
Assume AC for the compact integration bridge. The uniform joint density on the unit square refutes this at y=2. The constant uniform-on-(0,1) probability kernel is nevertheless a valid measurable conditional extension.
Facts & Assumptions
Given: The hypotheses and conventions in the statement refuted.
The conditional density theorem normalizes only finite positive marginal fibres and permits a fixed probability filler elsewhere. Conditional density formula.
An extension must have probability sections and measurable evaluations everywhere. Measure kernel and probability kernel.
The integral of one on [0,1] is computed by the primitive x. The second fundamental theorem: if is differentiable on with and is integrable, then .
The compact integral agrees with its Lebesgue integral under countable choice. A bounded Riemann integrable function on a closed bounded interval is Lebesgue measurable and has the same integral.
AC supplies the countable-choice bridge; no version selection is needed. The Axiom of Choice.
Tonelli computes this nonnegative product density and its marginal. Tonelli's theorem for nonnegative measurable functions on a sigma-finite product.
The square density and the interval density define measures. The indefinite integral of a nonnegative measurable function is a measure.
Counterexample
The density p is the indicator of a Borel rectangle. By [F3]–[F5], ; endpoints have measure zero by containment in intervals with arbitrarily small lengths. Tonelli [F6] gives total joint mass and marginal . The measure construction is [F7]. At y=2, for every x and . The asserted quotient is therefore 0/0, which is undefined, at every x on that fibre. Thus the claimed everywhere formula fails for a fully normalized bounded joint density.
Put and for all real y. By [F7] and the mass calculation, rho is a probability, and constant evaluations are measurable, proving [F2]. On this agrees with the density ratio. On its complement it is the supplied filler allowed by [F1]. Directly, for Borel A,B, with the last equality from [F6]. So the extension is a conditional law, including an everywhere probability section at y=2. For example , a chosen valid extension value, not a value of 0/0.
Depends on
- Conditional density formula
- Measure kernel and probability kernel
- The second fundamental theorem: if $G$ is differentiable on $[a,b]$ with $G' = f$ and $f$ is integrable, then $\int_a^b f = G(b)-G(a)$
- A bounded Riemann integrable function on a closed bounded interval is Lebesgue measurable and has the same integral
- The Axiom of Choice
- Tonelli's theorem for nonnegative measurable functions on a sigma-finite product
- The indefinite integral of a nonnegative measurable function is a measure
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
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Sources
- Durrett, Probability: Theory and Examples, fifth edition (standard reference, not scraped)
- Varadhan, Probability Theory, Chapter 4 (standard reference, not scraped)