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Independent random elements have product joint law
Statement
Let , and let for be independent random elements. Define
Then is a random element of , and its law is the finite product of the marginal laws:
Facts & Assumptions
Given: Independent random elements for .
Independence of random elements is equivalent to factorization on measurable rectangles. (Independent random elements are characterized by finite rectangle probabilities)
The law of a random element is a probability measure. (Law or distribution of a random element, The law of a random element is a probability measure)
For sigma-finite factors, the product measure is the unique measure on the product sigma-algebra having the rectangle formula. (For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique)
The finite product sigma-algebra is generated recursively by measurable rectangles. (The product sigma-algebra and its finite iterates)
Measures agreeing on a generating pi-system with an increasing finite-measure exhaustion from that pi-system are equal (Measures agreeing on a generating pi-system are equal under an increasing finite-measure exhaustion from that pi-system).
Proof
First prove that all-coordinate rectangles generate the recursively defined finite product sigma-algebra. This is immediate for one factor. Suppose it holds for factors, write and , and let be the sigma-algebra generated by the -coordinate rectangles. These rectangles belong to , so . Fix . The class contains , since is an all-coordinate rectangle. It is closed under complements relative to , because , and under countable unions because product with commutes with them. Hence it is a sigma-algebra containing all -coordinate rectangles. The induction hypothesis gives . As this holds for every , every binary rectangle generating lies in , proving equality and completing the finite induction.
Let . Preimages preserve complements and countable unions, so is a sigma-algebra. If is a measurable rectangle, then . By step 1.1, the product sigma-algebra is generated by these all-coordinate rectangles, so is measurable for .
For every measurable rectangle , [L1] gives
By step 2.1, the law is defined, and [L2] makes it a probability measure on the product sigma-algebra.
For every , put and, for , define recursively . For , step 3.1 already identifies with . For , repeated use of the rectangle formula in [L3] shows that for every measurable rectangle. Therefore and agree on all measurable rectangles.
All-coordinate rectangles are closed under intersections, because intersections are taken coordinatewise; they contain the whole product and generate its sigma-algebra by step 1.1. By step 4.1, and agree on this pi-system. Both have total mass one: [L2] gives this for the joint law, and the rectangle formula [L3] gives it at every stage of the product-law recursion. The constant exhaustion by the whole product therefore satisfies the finite-measure hypothesis of [L5], which gives This is the claimed product joint law.
Depends on
- Measures agreeing on a generating pi-system are equal under an increasing finite-measure exhaustion from that pi-system
- Independent random elements are characterized by finite rectangle probabilities
- Law or distribution of a random element
- The law of a random element is a probability measure
- For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique
- The product sigma-algebra and its finite iterates
Used by
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- Exponential tests and KL mass force posterior exclusion Lemma
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…and 47 more results.
Dependency tree · two levels
25 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
- Rick Durrett, Probability: Theory and Examples, 5th ed., Theorem 2.1.11 (standard reference, not scraped)
- S. R. S. Varadhan, Probability Theory, Lemma 3.1 (standard reference, not scraped)