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Simultaneous ae uniqueness of regular conditional distributions
Statement
Assume AC for the countable determining-algebra supplier. If are regular conditional distributions of the same standard-Borel-valued random element given , there is one with such that as measures for every .
Facts & Assumptions
Given: The hypotheses and conventions in the statement.
Each event evaluation is a real bounded conditional-expectation version. Regular conditional distribution.
The repaired in-batch standard-Borel construction supplies a countable algebra which generates the target sigma-algebra and determines finite measures. Existence of regular conditional distributions for standard borel targets.
The repaired local integral interface is monotone and additive for nonnegative functions and positively homogeneous without a zero-times-infinity product. Simultaneous rational conditional distribution function versions.
AC supplies real coding and the countable-choice algebra enumeration in the determining-algebra theorem. The Axiom of Choice.
A countable union of measurable null sets is null. Finite and countable subadditivity of measures.
Proof
Fix the countable determining algebra from [F2], whose AC hypothesis is [F4]. For , put and . By [F1], both are -valued and have equal integrals on every . For , the set belongs to . Monotonicity, additivity and positive homogeneity from [F3] give Equality of the two event integrals forces . The same argument with interchanged shows that is null. Their countable union is the measurable discrepancy set , so is null. This proves the needed scalar uniqueness locally, without importing conditional-expectation uniqueness.
The set is in and null by countability and [F5]. For any the two probability measures agree on every member of . Their total masses are finite, so the locally supplied determination assertion [F2] yields equality on the entire target sigma-algebra. This gives a single exceptional set independent of the target event.
Depends on
Used by
Dependency tree · two levels
34 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)