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The bounded-Lipschitz metric characterizes weak convergence on Polish spaces
Statement
Assume the Axiom of Choice. Let be a Polish space, and let be Borel probability measures on . Define
where , with value when has at most one point. Then
where is weak convergence of Borel probabilities (Weak convergence of borel probability measures). The implication from bounded-Lipschitz convergence to weak convergence is choice-free; the reverse implication below uses AC through Prokhorov's theorem.
Facts & Assumptions
Weak convergence of borel probability measures: means convergence of integrals for every bounded continuous real test function.
Portmanteau theorem: Weak convergence of Borel probabilities on a metric space is equivalent to the closed-set bound for every closed .
Prokhorov tightness theorem on polish spaces: Assume AC. A family of Borel probabilities on a Polish space is tight if and only if it is relatively sequentially compact for weak convergence.
Dominated convergence: If measurable functions converge pointwise almost everywhere and are dominated by one integrable function, their integrals converge.
Polish spaces are separable completely metrizable spaces: A Polish space is separable and completely metrizable.
Finite measures agreeing on a generating pi-system and on the whole space are equal: finite measures agreeing on a generating pi-system and on the whole space agree on its generated sigma-algebra.
The Axiom of Choice: AC is used only for the Prokhorov implication from relative sequential compactness to tightness in step 2.2; step 1.2 proves the reverse metric implication without AC.
Given: The objects in the statement and AC for step 2.2.
Proof
Nonnegativity, symmetry and the triangle inequality for follow by applying these properties to the difference of the two test integrals and then taking the supremum. If , fix a nonempty closed and put . The function is an admissible test, so . By [F4], these integrals decrease to and , respectively. The two probabilities therefore agree on every closed set, including the empty set. Closed sets form a pi-system generating the Borel sigma-algebra, and both total masses are one, so [F6] gives . Thus is a metric; this argument uses no choice.
Assume , and fix a nonempty closed set . For every integer , the function satisfies and . Hence is admissible in the definition of , so . Since , it follows that for every .
Now assume , and put . Any sequence chosen from either has a member repeated infinitely often, yielding a constant convergent subsequence, or has a subsequence of the form with , which converges weakly to . Therefore is relatively sequentially compact for weak convergence.
The functions decrease pointwise to and are bounded by the integrable constant function under the probability . By [F4], , so the closed-set bound holds; it is trivial for . The Portmanteau theorem [F2] gives weak convergence in the sense of [F1].
By [F5], is Polish; therefore [F3] applies under [A1] and is tight. Thus for every there is a compact such that .
Fix . Compactness gives a finite -net of . Quantize each value into finitely many bins of width at most . There are finitely many resulting value patterns for the bounded 1-Lipschitz test class; choose one representative from each nonempty pattern. If and share a pattern, then for a net point gives .
For any admissible , choose the representative from its pattern. The integral difference is bounded by The maximum tends to zero by [F1], because there are finitely many bounded continuous representatives, and the last two terms are at most by step 2.2. Taking the supremum over , then the limit superior in , gives . Letting proves the reverse implication.
Depends on
Used by
- Continuous images of the scaled posterior converge weakly Corollary
- The scaled posterior converges weakly on the negative Sobolev space Corollary
- Projected empirical bridge CLT in the little-o multiscale space Theorem
- Type-H histogram posterior weak Bernstein–von Mises theorem Theorem
- Type-S density posteriors satisfy the weak multiscale Bernstein–von Mises theorem Theorem
Dependency tree · two levels
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Sources
- Giné and Nickl, Mathematical Foundations of Infinite-Dimensional Statistical Models, §3.7 and §7.3.4, printed pp. 246–247 and 594–595 (standard reference, not scraped)
- Castillo and Nickl, Nonparametric Bernstein–von Mises theorems in Gaussian white noise, Definition 1, printed pp. 2003–2004 (standard reference, not scraped)