In mathematics, the concept of a measure is a generalization and formalization of geometrical measures (length, area, volume) and other common notions, such as magnitude, mass, and probability of events. These seemingly distinct concepts have many similarities and can often be treated together in a single mathematical context. Measures are foundational in probability theory, integration theory, and can be generalized to assume negative values, as with electrical charge. Far-reaching generalizations (such as spectral measures and positive operator-valued measuress) of measure are widely used in quantum physics and physics in general.

The intuition behind this concept dates back to Ancient Greece, when Archimedes tried to calculate the area of a circle. But it was not until the late 19th and early 20th centuries that measure theory became a branch of mathematics. The foundations of modern measure theory were laid in the works of Émile Borel, Henri Lebesgue, Nikolai Luzin, Johann Radon, Constantin Carathéodory, and Maurice Fréchet, among others. According to Thomas W. Hawkins Jr., "It was primarily through the theory of multiple integrals and, in particular the work of Camille Jordan that the importance of the notion of measurability was first recognized."

Definition

a σ-algebra that are "measurable". A set function, used to denote elements greater than all other (so-called finite) elements, is called a measure if the following conditions hold:

• Countable additivity (or σ-additivity): For all countable of pairwise disjoint sets

in the extended reals.

is called a signed measure.

is called a measurable space are called measurable sets.

A triple is called a measure space. A probability measure A probability space is a measure space with a probability measure.

For measure spaces that are also topological spaces various compatibility conditions can be placed for the measure and the topology. Most measures met in practice in analysis (and in many cases also in probability theory) are Radon measures (usually defined on Hausdorff spaces). When working with locally compact Hausdorff spaces, Radon measures have an alternative, equivalent definition in terms of linear functionals on the locally convex topological vector space of continuous functions with compact support. This approach is taken by Bourbaki (2004) and a number of other sources. For more details, see the article on Radon measures.

Instances

Some important measures are listed here.

• The counting measure

• The Lebesgue measure is a complete translation-invariant measure on a σ-algebra containing the intervals; and every other measure with these properties extends the Lebesgue measure.

• The arc length-algebra they generate. It can be called angle measure since the arc length of an interval equals the angle it supports. This measure is invariant under rotations preserving the circle. Similarly, hyperbolic angle measure is invariant under squeeze mapping.

• The Haar measure for a locally compact topological group) its Haar measure is the angle measure. For a discrete group the counting measure is a Haar measure.

• Every (pseudo) Riemannian manifold is the usual Lebesgue measure.

• The Hausdorff measure is a generalization of the Lebesgue measure to sets with non-integer dimension, in particular, fractal sets.

• Every probability space gives rise to a measure which takes the value 1 on the whole space (and therefore takes all its values in the unit interval [0, 1]). Such a measure is called a probability measure or distribution. See the list of probability distributions for instances.

• The Dirac measure δa (cf. Dirac delta function) is given by δa(S) = χS(a), where χS is the indicator function and 0 otherwise.

Other 'named' measures used in various theories include: Borel measure, Jordan measure, ergodic measure, Gaussian measure, Baire measure, Radon measure, Young measure, and Loeb measure.

In physics an example of a measure is spatial distribution of mass (see for example, gravity potential), or another non-negative extensive property, conserved (see conservation law for a list of these) or not. Negative values lead to signed measures, see "generalizations" below.

• Liouville measure, known also as the natural volume form on a symplectic manifold, is useful in classical statistical and Hamiltonian mechanics.

• Gibbs measure is widely used in statistical mechanics, often under the name canonical ensemble.

Basic properties

be a measure.

Monotonicity

Measure of countable unions and intersections

Countable subadditivity

For any countable sequence

Continuity from below

) then the union

Continuity from above

) then the intersection

which all have infinite Lebesgue measure, but the intersection is empty.

Other properties

Completeness

is called a null set A subset of a null set is called a negligible set. A negligible set need not be measurable, but every measurable negligible set is automatically a null set. A measure is called complete if every negligible set is measurable.

that is, such that the symmetric difference

"Dropping the edge"

for almost all This property is used in connection with Lebesgue integral.

Additivity

to be the supremum of all the sums of finitely many of them.

The second condition is equivalent to the statement that the ideal-complete.

Sigma-finite measures

). is called σ-finite can be decomposed into a countable union of measurable sets of finite measure.

For example, the real numbers with the standard Lebesgue measure are σ-finite but not finite. The natural numbers are also σ-finite with respect to counting measure. However, the real numbers are not σ-finite with respect to counting measure.

Strictly localizable measures

Semifinite measures

is semifinite

Semifinite measures generalize sigma-finite measures, in such a way that some big theorems of measure theory that hold for sigma-finite but not arbitrary measures can be extended with little modification to hold for semifinite measures. (To-do: add examples of such theorems; cf. the talk page.)

Basic examples

• Every sigma-finite measure is semifinite.

is

• is countable; and

• gives an example of a semifinite measure that is not sigma-finite.)

• be the Borel sigma-algebra Then the Hausdorff measure is semifinite.

• be the Borel sigma-algebra Then the packing measure is semifinite.

Involved example

It can be shown there is a greatest measure with these two properties: We say the semifinite part defined in the above theorem. We give some nice, explicit formulas, which some authors may take as definition, for the semifinite part:

Non-examples

Every measure that is not the zero measure is not semifinite. (Here, we say measure measures that are not zero measures.

is a measure.

• is a measure.

Involved non-example

We say the part

Results regarding semifinite measures

• is injective. (This result has import in the study of the L^1=L_\mathbb{F}^1(\mu).)

• is Hausdorff.

• are semifinite if and only if (\mu\times_\text{cld}\nu) is the measure defined in Theorem 39.1 in Berberian '65.)

Localizable measures

Localizable measures are a special case of semifinite measures and a generalization of sigma-finite measures.

• ).

s-finite measures

A measure is said to be s-finite if it is a countable sum of finite measures. S-finite measures are more general than sigma-finite ones and have applications in the theory of stochastic processes.

Non-measurable sets

If the axiom of choice is assumed to be true, it can be proved that not all subsets of Euclidean space are Lebesgue measurable; examples of such sets include the Vitali set, and the non-measurable sets postulated by the Hausdorff paradox and the Banach–Tarski paradox.

Generalizations

For certain purposes, it is useful to have a "measure" whose values are not restricted to the non-negative reals or infinity. For instance, a countably additive set function with values in the (signed) real numbers is called a signed measure, while such a function with values in the complex numbers is called a complex measure. Observe, however, that complex measure is necessarily of finite variation, hence complex measures include finite signed measures but not, for example, the Lebesgue measure.

Measures that take values in Banach spaces have been studied extensively. A measure that takes values in the set of self-adjoint projections on a Hilbert space is called a projection-valued measure; these are used in functional analysis for the spectral theorem. When it is necessary to distinguish the usual measures which take non-negative values from generalizations, the term positive measure is used. Positive measures are closed under conical combination but not general linear combination, while signed measures are the linear closure of positive measures. More generally see measure theory in topological vector spaces.

Another generalization is the finitely additive measure, also known as a content. This is the same as a measure except that instead of requiring countable additivity we require only finite additivity. Historically, this definition was used first. It turns out that in general, finitely additive measures are connected with notions such as Banach limits, the dual of L^\infty and the Stone–Čech compactification. All these are linked in one way or another to the axiom of choice. Contents remain useful in certain technical problems in geometric measure theory; this is the theory of Banach measures.

A charge is a generalization in both directions: it is a finitely additive, signed measure. (Cf. ba space for information about bounded charges, where we say a charge is bounded to mean its range is a bounded subset of R.)

See also

• Abelian von Neumann algebra

• Almost everywhere

• Carathéodory's extension theorem

• Content (measure theory)

• Fubini's theorem

• Fatou's lemma

• Fuzzy measure theory

• Geometric measure theory

• Hausdorff measure

• Inner measure

• Lebesgue integration

• Lebesgue measure

• Lorentz space

• Lifting theory

• Measurable cardinal

• Measurable function

• Minkowski content

• Outer measure

• Product measure

• Pushforward measure

• Random measure

• Regular measure

• Vector measure

• Valuation (measure theory)

• Volume form

Notes

Bibliography

• Robert G. Bartle (1995) The Elements of Integration and Lebesgue Measure, Wiley Interscience.

• Chapter III.

• Herbert Federer (1969) Geometric Measure Theory, Die Grundlehren der mathematischen Wissenschaften, Band 153 Springer-Verlag

• Second printing.

• R. Duncan Luce and Louis Narens (1987). "measurement, theory of", The New Palgrave: A Dictionary of Economics, v. 3, pp.428–32.

• The first edition was published with Part B: Functional Analysis as a single volume:

• M. E. Munroe, 1953. Introduction to Measure and Integration. Addison Wesley.

• First printing. There is a later (2017) second printing. Though usually there is little difference between the first and subsequent printings, in this case the second printing not only deletes from page 53 the Exercises 36, 40, 41, and 42 of Chapter 2 but also offers a (slightly, but still substantially) different presentation of part (ii) of Exercise 17.8. (The second printing's presentation of part (ii) of Exercise 17.8 (on the Luther decomposition) agrees with usual presentations, whereas the first printing's presentation provides a fresh perspective.)

• Shilov, G. E., and Gurevich, B. L., 1978. Integral, Measure, and Derivative: A Unified Approach, Richard A. Silverman, trans. Dover Publications. . Emphasizes the Daniell integral.

References

External links

• Tutorial: Measure Theory for Dummies