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In [[mathematics]], the '''Lévy–Prokhorov metric''' (sometimes known just as the '''Prokhorov metric''') is a [[metric (mathematics)|metric]] (i.e., a definition of distance) on the collection of [[probability measure]]s on a given [[metric space]]. It is named after the French mathematician [[Paul Lévy (mathematician)|Paul Lévy]] and the Soviet mathematician [[Yuri Vasilevich Prokhorov]]; Prokhorov introduced it in 1956 as a generalization of the earlier [[Lévy metric]]. | |||
==Definition== | |||
Let <math>(M, d)</math> be a [[metric space]] with its [[Borel sigma algebra]] <math>\mathcal{B} (M)</math>. Let <math>\mathcal{P} (M)</math> denote the collection of all [[probability measure]]s on the [[measurable space]] <math>(M, \mathcal{B} (M))</math>. | |||
For a [[subset]] <math>A \subseteq M</math>, define the [[epsilon-neighborhood|ε-neighborhood]] of <math>A</math> by | |||
:<math>A^{\varepsilon} := \{ p \in M ~|~ \exists q \in A, \ d(p, q) < \varepsilon \} = \bigcup_{p \in A} B_{\varepsilon} (p).</math> | |||
where <math>B_{\varepsilon} (p)</math> is the [[open ball]] of radius <math>\varepsilon</math> centered at <math>p</math>. | |||
The '''Lévy–Prokhorov metric''' <math>\pi : \mathcal{P} (M)^{2} \to [0, + \infty)</math> is defined by setting the distance between two probability measures <math>\mu</math> and <math>\nu</math> to be | |||
:<math>\pi (\mu, \nu) := \inf \left\{ \varepsilon > 0 ~|~ \mu(A) \leq \nu (A^{\varepsilon}) + \varepsilon \ \text{and} \ \nu (A) \leq \mu (A^{\varepsilon}) + \varepsilon \ \text{for all} \ A \in \mathcal{B}(M) \right\}.</math> | |||
For probability measures clearly <math>\pi (\mu, \nu) \le 1</math>. | |||
Some authors omit one of the two inequalities or choose only [[open set|open]] or [[closed set|closed]] <math>A</math>; either inequality implies the other, but restricting to open/closed sets changes the metric so defined. | |||
==Properties== | |||
* If <math>(M, d)</math> is [[separable space|separable]], convergence of measures in the Lévy–Prokhorov metric is equivalent to [[weak convergence of measures]]. Thus, <math>\pi</math> is a [[Metrization theorem|metrization]] of the topology of weak convergence. | |||
* The metric space <math>\left( \mathcal{P} (M), \pi \right)</math> is [[separable space|separable]] [[if and only if]] <math>(M, d)</math> is separable. | |||
* If <math>\left( \mathcal{P} (M), \pi \right)</math> is [[complete space|complete]] then <math>(M, d)</math> is complete. If all the measures in <math>\mathcal{P} (M)</math> have separable [[support (measure theory)|support]], then the converse implication also holds: if <math>(M, d)</math> is complete then <math>\left( \mathcal{P} (M), \pi \right)</math> is complete. | |||
* If <math>(M, d)</math> is separable and complete, a subset <math>\mathcal{K} \subseteq \mathcal{P} (M)</math> is [[relatively compact]] if and only if its <math>\pi</math>-closure is <math>\pi</math>-compact. | |||
==See also== | |||
* [[Lévy metric]] | |||
* [[Wasserstein metric]] | |||
==References== | |||
* {{cite book | author=Billingsley, Patrick | title=Convergence of Probability Measures | publisher=John Wiley & Sons, Inc., New York | year=1999 | isbn=0-471-19745-9 | oclc=41238534}} | |||
* {{springer|author=Zolotarev, V.M.|id=l/l058320|title=Lévy–Prokhorov metric}} | |||
{{DEFAULTSORT:Levy-Prokhorov metric}} | |||
[[Category:Measure theory]] | |||
[[Category:Metric geometry]] | |||
[[Category:Probability theory]] |
Revision as of 15:31, 20 April 2013
In mathematics, the Lévy–Prokhorov metric (sometimes known just as the Prokhorov metric) is a metric (i.e., a definition of distance) on the collection of probability measures on a given metric space. It is named after the French mathematician Paul Lévy and the Soviet mathematician Yuri Vasilevich Prokhorov; Prokhorov introduced it in 1956 as a generalization of the earlier Lévy metric.
Definition
Let be a metric space with its Borel sigma algebra . Let denote the collection of all probability measures on the measurable space .
For a subset , define the ε-neighborhood of by
where is the open ball of radius centered at .
The Lévy–Prokhorov metric is defined by setting the distance between two probability measures and to be
For probability measures clearly .
Some authors omit one of the two inequalities or choose only open or closed ; either inequality implies the other, but restricting to open/closed sets changes the metric so defined.
Properties
- If is separable, convergence of measures in the Lévy–Prokhorov metric is equivalent to weak convergence of measures. Thus, is a metrization of the topology of weak convergence.
- The metric space is separable if and only if is separable.
- If is complete then is complete. If all the measures in have separable support, then the converse implication also holds: if is complete then is complete.
- If is separable and complete, a subset is relatively compact if and only if its -closure is -compact.
See also
References
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