This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Find sources: "Finite measure" – news · newspapers · books · scholar · JSTOR (January 2018) (Learn how and when to remove this message) |
In measure theory, a branch of mathematics, a finite measure or totally finite measure[1] is a special measure that always takes on finite values. Among finite measures are probability measures. The finite measures are often easier to handle than more general measures and show a variety of different properties depending on the sets they are defined on.
Definition
[edit]A measure 👁 {\displaystyle \mu }
on measurable space 👁 {\displaystyle (X,{\mathcal {A}})}
is called a finite measure if it satisfies
By the monotonicity of measures, this implies
If 👁 {\displaystyle \mu }
is a finite measure, the measure space 👁 {\displaystyle (X,{\mathcal {A}},\mu )}
is called a finite measure space or a totally finite measure space.[1]
Properties
[edit]General case
[edit]For any measurable space, the finite measures form a convex cone in the Banach space of signed measures with the total variation norm. Important subsets of the finite measures are the sub-probability measures, which form a convex subset, and the probability measures, which are the intersection of the unit sphere in the normed space of signed measures and the finite measures.
Topological spaces
[edit]If 👁 {\displaystyle X}
is a Hausdorff space and 👁 {\displaystyle {\mathcal {A}}}
contains the Borel 👁 {\displaystyle \sigma }
-algebra then every finite measure is also a locally finite Borel measure.
Metric spaces
[edit]If 👁 {\displaystyle X}
is a metric space and the 👁 {\displaystyle {\mathcal {A}}}
is again the Borel 👁 {\displaystyle \sigma }
-algebra, the weak convergence of measures can be defined. The corresponding topology is called weak topology and is the initial topology of all bounded continuous functions on 👁 {\displaystyle X}
. The weak topology corresponds to the weak* topology in functional analysis. If 👁 {\displaystyle X}
is also separable, the weak convergence is metricized by the Lévy–Prokhorov metric.[2]
Polish spaces
[edit]If 👁 {\displaystyle X}
is a Polish space and 👁 {\displaystyle {\mathcal {A}}}
is the Borel 👁 {\displaystyle \sigma }
-algebra, then every finite measure is a regular measure and therefore a Radon measure.[3]
If 👁 {\displaystyle X}
is Polish, then the set of all finite measures with the weak topology is Polish too.[4]
See also
[edit]References
[edit]- ^ a b Anosov, D.V. (2001) [1994], "Measure space", Encyclopedia of Mathematics, EMS Press
- ^ Klenke, Achim (2008). Probability Theory. Berlin: Springer. p. 252. doi:10.1007/978-1-84800-048-3. ISBN 978-1-84800-047-6.
- ^ Klenke, Achim (2008). Probability Theory. Berlin: Springer. p. 248. doi:10.1007/978-1-84800-048-3. ISBN 978-1-84800-047-6.
- ^ Kallenberg, Olav (2017). Random Measures, Theory and Applications. Probability Theory and Stochastic Modelling. Vol. 77. Switzerland: Springer. p. 112. doi:10.1007/978-3-319-41598-7. ISBN 978-3-319-41596-3.
