How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Weak-star compactness of probability measures
Statement
Assume the ultrafilter lemma. If is compact Hausdorff, the regular Borel probability measures on form a compact space for convergence against continuous real- or complex-valued functions, using the corresponding real or complex dual of .
Facts & Assumptions
Given: The ultrafilter lemma and a compact Hausdorff space .
Under the ultrafilter lemma, the closed dual unit ball is weak-star compact (Banach–Alaoglu).
Every bounded positive functional on real has a unique finite regular representing measure whose mass is its norm (Positive C_0(X) functionals have finite regular representing measures).
Bounded complex functionals on are uniquely the integrals against finite regular complex Borel measures, with norm equal to total variation (The bounded complex dual of C_0(X) is regular complex measures).
Regular Borel measures are inner regular on every Borel set (Regular Borel measure on an LCH space).
Every compact space is locally compact (Locally compact topological space: every point has a compact neighbourhood; and what this says in a metric space).
The spaces and are defined by compact support and compact superlevel sets (Compact support, , and ).
A closed subset of a compact space is compact (A closed subspace of a compact space is compact, and a finite union of compact subspaces is compact, claim 1).
Proof
If , no measure can have total mass one, so the probability space is empty and compact. Hence assume . By [F5], compactness makes locally compact, and it is Hausdorff by hypothesis. Every continuous function on compact has compact support and compact closed superlevel sets, so [F6] gives .
A regular Borel probability defines . For real or complex , , while ; hence and . It is positive on nonnegative real-valued .
Conversely, let satisfy and for every nonnegative real-valued . In the real case [F2] represents by a finite regular measure in the sense of [F4], and . In the complex case restrict to real-valued functions, apply [F2], and use complex linearity to recover ; uniqueness in [F3] identifies this same positive probability measure.
Thus probabilities correspond exactly to the slice of cut out by and by for all nonnegative real-valued . Each condition is weak-star closed because it is the inverse image of the closed set or under one evaluation; arbitrary intersections remain closed.
By [F1], the dual ball is weak-star compact under the ultrafilter lemma. Hence its closed subset is compact by [F7]. Under the two representation directions above, the weak-star topology is exactly convergence of for every continuous test function , so the probability measures are compact in the asserted topology.
The initial reduction proves the empty case, and the closed-slice construction proves both scalar-field cases for nonempty compact Hausdorff .
Depends on
- Banach–Alaoglu
- Positive C_0(X) functionals have finite regular representing measures
- The bounded complex dual of C_0(X) is regular complex measures
- Regular Borel measure on an LCH space
- Locally compact topological space: every point has a compact neighbourhood; and what this says in a metric space
- Compact support, $C_c(X)$, and $C_0(X)$
- A closed subspace of a compact space is compact, and a finite union of compact subspaces is compact
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
31 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
- Bühler–Salamon, Functional Analysis (standard reference, not scraped)
- Gerald Teschl, Topics in Real and Functional Analysis (standard reference, not scraped)