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Conditional-kernel tail and atom evaluations are measurable
Statement
Let be a probability kernel from to . Define Both maps are -measurable. If and are measurable random elements of and on one probability space, and is any measurable real random variable on that space, then , , and are measurable. The middle expression equals .
Facts & Assumptions
Given: A probability kernel , measurable random elements , and a measurable real random variable .
For every measurable target set , is measurable, and each section is a probability measure. Measure kernel and probability kernel
Product sigma-algebras are generated by measurable rectangles. The product sigma-algebra and its finite iterates
The rationals are countable and dense in . is countably infinite, Both and are dense in , and every nonempty open subset of is uncountable
The Borel sigma-algebra is generated by open sets, so every open interval is Borel. The Borel sigma-algebra of a topological space
Integrating a nonnegative product-measurable function against a probability kernel gives a measurable function of the kernel parameter, with infinity allowed. Measurability of integration against a kernel
A random element is a measurable map into its specified measurable space. Random elements and real random variables
Continuous maps have Borel-measurable preimages, and measurable maps compose. A continuous map has Borel preimages of Borel sets, Composition with a Borel measurable outer map preserves measurability
Proof
Let be the first-coordinate projection and define . The projection is measurable because for every ; hence [F1] and [F2] show that is a probability kernel from to .
The relation is : density in [F3] supplies a rational strictly between and , and every point in a displayed rectangle has . The relation has the analogous representation . These are countable unions of Borel rectangles by [F3] and [F4], and the equality relation is their joint complement. Therefore all three relations belong to . The coordinate map is measurable because inverse images of generating rectangles are ; taking inverse images now proves the two relation indicators in the domain of are product-measurable.
Apply [F5] to and the two indicator functions of the relation sets in step 1.2. Their section integrals are and , so both maps are product-measurable.
For , fix a bijection supplied by [F3]. Each rational open box is encoded by a finite tuple of natural numbers, and iterating the injection shows there are only countably many such boxes. Density of then shows they form a base of : each point of an open set lies in such a box contained in that set. Thus each open set is a countable union of rectangles in the finite product sigma-algebra, giving . Conversely, each coordinate projection is continuous, so [F7] makes Borel for every ; finite intersections show every measurable rectangle is Borel, giving the reverse inclusion. Hence the Borel product and Euclidean Borel sigma-algebras agree in both dimensions. The map is measurable because the inverse image of each generating rectangle is ; therefore and are measurable by [F6] and [F7]. The sets and are disjoint and their union is , so finite additivity gives . The map is continuous by . The map is continuous because , and is bounded in a sufficiently small neighborhood of . The pairs and triples of the real measurable functions already obtained and are measurable because inverse images of generating product rectangles are finite intersections of coordinate preimages. The established Borel-product equalities make these continuous maps measurable by [F7]. Composition by [F7] proves measurability of the asserted sums.
Depends on
- Measure kernel and probability kernel
- The product sigma-algebra and its finite iterates
- The Borel sigma-algebra of a topological space
- $\mathbb{Q}$ is countably infinite
- Both $\mathbb{Q}$ and $\mathbb{R} \setminus \mathbb{Q}$ are dense in $\mathbb{R}$, and every nonempty open subset of $\mathbb{R}$ is uncountable
- Measurability of integration against a kernel
- Random elements and real random variables
- A continuous map has Borel preimages of Borel sets
- Composition with a Borel measurable outer map preserves measurability
Used by
- Randomized conditional upper-tail p-values Definition
Dependency tree · two levels
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Sources
- Ryan Martin, Lecture Notes on Advanced Statistical Theory, §2.3, PDF pp. 33–35 (standard reference, not scraped)
- A. J. Brockwell, Universal Residuals: A Multivariate Transformation, Lemma 2 and Theorem 1 (standard reference, not scraped)