Borel measure

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In mathematics, specifically in measure theory, a Borel measure on a topological space is a measure that is defined on all open sets (and thus on all Borel sets).[1] Some authors require additional restrictions on the measure, as described below.

Formal definition[edit]

Let be a locally compact Hausdorff space, and let be the smallest σ-algebra that contains the open sets of ; this is known as the σ-algebra of Borel sets. A Borel measure is any measure defined on the σ-algebra of Borel sets.[2] Some authors require in addition that is locally compact, meaning that for every compact set . If a Borel measure is both inner regular and outer regular, it is called a regular Borel measure (some authors also require it to be tight). If is both inner regular and locally finite, it is called a Radon measure.

On the real line[edit]

The real line with its usual topology is a locally compact Hausdorff space, hence we can define a Borel measure on it. In this case, is the smallest σ-algebra that contains the open intervals of . While there are many Borel measures μ, the choice of Borel measure which assigns for every half-open interval is sometimes called "the" Borel measure on . This measure turns out to be the restriction on the Borel σ-algebra of the Lebesgue measure , which is a complete measure and is defined on the Lebesgue σ-algebra. The Lebesgue σ-algebra is actually the completion of the Borel σ-algebra, which means that it is the smallest σ-algebra which contains all the Borel sets and has a complete measure on it. Also, the Borel measure and the Lebesgue measure coincide on the Borel sets (i.e., for every Borel measurable set, where is the Borel measure described above).

Product spaces[edit]

If X and Y are second-countable, Hausdorff topological spaces, then the set of Borel subsets of their product coincides with the product of the sets of Borel subsets of X and Y.[3] That is, the Borel functor

from the category of second-countable Hausdorff spaces to the category of measurable spaces preserves finite products.


Lebesgue–Stieltjes integral[edit]

The Lebesgue–Stieltjes integral is the ordinary Lebesgue integral with respect to a measure known as the Lebesgue–Stieltjes measure, which may be associated to any function of bounded variation on the real line. The Lebesgue–Stieltjes measure is a regular Borel measure, and conversely every regular Borel measure on the real line is of this kind.[4]

Laplace transform[edit]

One can define the Laplace transform of a finite Borel measure μ on the real line by the Lebesgue integral[5]

An important special case is where μ is a probability measure or, even more specifically, the Dirac delta function. In operational calculus, the Laplace transform of a measure is often treated as though the measure came from a distribution function f. In that case, to avoid potential confusion, one often writes

where the lower limit of 0 is shorthand notation for

This limit emphasizes that any point mass located at 0 is entirely captured by the Laplace transform. Although with the Lebesgue integral, it is not necessary to take such a limit, it does appear more naturally in connection with the Laplace–Stieltjes transform.

Hausdorff dimension and Frostman's lemma[edit]

Given a Borel measure μ on a metric space X such that μ(X) > 0 and μ(B(x, r)) ≤ rs holds for some constant s > 0 and for every ball B(x, r) in X, then the Hausdorff dimension dimHaus(X) ≥ s. A partial converse is provided by Frostman's lemma:[6]

Lemma: Let A be a Borel subset of Rn, and let s > 0. Then the following are equivalent:

  • Hs(A) > 0, where Hs denotes the s-dimensional Hausdorff measure.
  • There is an (unsigned) Borel measure μ satisfying μ(A) > 0, and such that
holds for all x ∈ Rn and r > 0.

Cramér–Wold theorem[edit]

The Cramér–Wold theorem in measure theory states that a Borel probability measure on is uniquely determined by the totality of its one-dimensional projections.[7] It is used as a method for proving joint convergence results. The theorem is named after Harald Cramér and Herman Ole Andreas Wold.


  1. ^ D. H. Fremlin, 2000. Measure Theory. Torres Fremlin.
  2. ^ Alan J. Weir (1974). General integration and measure. Cambridge University Press. pp. 158–184. ISBN 0-521-29715-X. 
  3. ^ Vladimir I. Bogachev. Measure Theory, Volume 1. Springer Science & Business Media, Jan 15, 2007
  4. ^ Halmos, Paul R. (1974), Measure Theory, Berlin, New York: Springer-Verlag, ISBN 978-0-387-90088-9 
  5. ^ Feller 1971, §XIII.1
  6. ^ Rogers, C. A. (1998). Hausdorff measures. Cambridge Mathematical Library (Third ed.). Cambridge: Cambridge University Press. pp. xxx+195. ISBN 0-521-62491-6. 
  7. ^ K. Stromberg, 1994. Probability Theory for Analysts. Chapman and Hall.

Further reading[edit]

External links[edit]