In measure theory Prokhorov’s theorem relates tightness of measures to relative compactness (and hence weak convergence) in the space of probability measures. It is credited to the Soviet mathematician Yuri Vasilyevich Prokhorov, who considered probability measures on complete separable metric spaces. The term "Prokhorov’s theorem" is also applied to later generalizations to either the direct or the inverse statements.
Statement of the theorem
- A collection of probability measures is tight if and only if the closure of is sequentially compact in the space equipped with the topology of weak convergence.
- The space with the topology of weak convergence is metrizable.
- Suppose that in addition, is a complete metric space (so that is a Polish space). There is a complete metric on equivalent to the topology of weak convergence; moreover, is tight if and only if the closure of in is compact.
For Euclidean spaces we have that:
- If is a tight sequence in (the collection of probability measures on -dimensional Euclidean space), then there exist a subsequence and a probability measure such that converges weakly to .
- If is a tight sequence in such that every weakly convergent subsequence has the same limit , then the sequence converges weakly to .
Theorem: Suppose that is a complete separable metric space and is a family of Borel complex measures on .The following statements are equivalent:
- is sequentially compact; that is, every sequence has a weakly convergent subsequence.
- is tight and uniformly bounded in total variation norm.
Since Prokhorov's theorem expresses tightness in terms of compactness, the Arzelà-Ascoli theorem is often used to substitute for compactness: in function spaces, this leads to a characterization of tightness in terms of the modulus of continuity or an appropriate analogue — see tightness in classical Wiener space and tightness in Skorokhod space.
There are several deep and non-trivial extensions to Prokhorov's theorem. However, those results do not overshadow the importance and the relevance to applications of the original result.
- Billingsley, Patrick (1999). Convergence of Probability Measures. New York, NY: John Wiley & Sons, Inc. ISBN 0-471-19745-9.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
- Bogachev, Vladimir (2006). Measure Theory Vol 1 and 2. Springer. ISBN 978-3-540-34513-8.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
- Lua error in Module:Citation/CS1/Identifiers at line 47: attempt to index field 'wikibase' (a nil value).
- Dudley, Richard. M. (1989). Real analysis and Probability. Chapman & Hall. ISBN 0-412-05161-3.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>