Informally, a measure has the property of being
monotone in the sense that if
A is a
subset of
B, the measure of
A is less than or equal to the measure of
B. Furthermore, the measure of the
empty set is required to be 0.
In mathematical analysis, a measure on a set is a systematic way to assign a number to each suitable subset of that set, intuitively interpreted as its size. In this sense, a measure is a generalization of the concepts of length, area, and volume. A particularly important example is the Lebesgue measure on a Euclidean space, which assigns the conventional length, area, and volume of Euclidean geometry to suitable subsets of the ndimensional Euclidean space R^{n}. For instance, the Lebesgue measure of the interval [0, 1] in the real numbers is its length in the everyday sense of the word – specifically, 1.
Technically, a measure is a function that assigns a nonnegative real number or +∞ to (certain) subsets of a set X (see Definition below). It must assign 0 to the empty set and be (countably) additive: the measure of a 'large' subset that can be decomposed into a finite (or countable) number of 'smaller' disjoint subsets, is the sum of the measures of the "smaller" subsets. In general, if one wants to associate a consistent size to each subset of a given set while satisfying the other axioms of a measure, one only finds trivial examples like the counting measure. This problem was resolved by defining measure only on a subcollection of all subsets; the socalled measurable subsets, which are required to form a σalgebra. This means that countable unions, countable intersections and complements of measurable subsets are measurable. Nonmeasurable sets in a Euclidean space, on which the Lebesgue measure cannot be defined consistently, are necessarily complicated in the sense of being badly mixed up with their complement.^{[1]} Indeed, their existence is a nontrivial consequence of the axiom of choice.
Measure theory was developed in successive stages during the late 19th and early 20th centuries by Émile Borel, Henri Lebesgue, Johann Radon and Maurice Fréchet, among others. The main applications of measures are in the foundations of the Lebesgue integral, in Andrey Kolmogorov's axiomatisation of probability theory and in ergodic theory. In integration theory, specifying a measure allows one to define integrals on spaces more general than subsets of Euclidean space; moreover, the integral with respect to the Lebesgue measure on Euclidean spaces is more general and has a richer theory than its predecessor, the Riemann integral. Probability theory considers measures that assign to the whole set the size 1, and considers measurable subsets to be events whose probability is given by the measure. Ergodic theory considers measures that are invariant under, or arise naturally from, a dynamical system.
Contents

Definition 1

Examples 2

Properties 3

Monotonicity 3.1

Measures of infinite unions of measurable sets 3.2

Measures of infinite intersections of measurable sets 3.3

Sigmafinite measures 4

Completeness 5

Additivity 6

Nonmeasurable sets 7

Generalizations 8

See also 9

References 10

Bibliography 11

External links 12
Definition
Countable additivity of a measure μ: The measure of a countable disjunctive union is the same as the sum of all measures of each subset.
Let X be a set and Σ a σalgebra over X. A function μ from Σ to the extended real number line is called a measure if it satisfies the following properties:

Nonnegativity: For all E in Σ: μ(E) ≥ 0.

Null empty set: μ(∅) = 0.

\mu\left(\bigcup_{i \in \mathbf{N}} E_i\right ) = \sum_{i \in \mathbf{N}} \mu\left(E_i\right).
One may require that at least one set E has finite measure. Then the null set automatically has measure zero because of countable additivity, because \mu(E)=\mu(E \cup \varnothing) = \mu(E) + \mu(\varnothing), so \mu(\varnothing) = \mu(E)  \mu(E) = 0.
If only the second and third conditions of the definition of measure above are met, and μ takes on at most one of the values ±∞, then μ is called a signed measure.
The pair (X, Σ) is called a measurable space, the members of Σ are called measurable sets. If \left(X, \Sigma_X\right) and \left(Y, \Sigma_Y\right) are two measurable spaces, then a function f : X \to Y is called measurable if for every Ymeasurable set B \in \Sigma_Y, the inverse image is Xmeasurable – i.e.: f^{(1)}(B) \in \Sigma_X. The composition of measurable functions is measurable, making the measurable spaces and measurable functions a category, with the measurable spaces as objects and the set of measurable functions as arrows.
A triple (X, Σ, μ) is called a measure space. A probability measure is a measure with total measure one – i.e. μ(X) = 1. 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. Radon measures have an alternative definition in terms of linear functionals on the locally convex 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.
Examples
Some important measures are listed here.
Other 'named' measures used in various theories include: Borel measure, Jordan measure, ergodic measure, Euler measure, Gaussian measure, Baire measure, Radon measure, Young measure, and strong measure zero.
In physics an example of a measure is spatial distribution of mass (see e.g., gravity potential), or another nonnegative 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.
Properties
Several further properties can be derived from the definition of a countably additive measure.
Monotonicity
A measure μ is monotonic: If E_{1} and E_{2} are measurable sets with E_{1} ⊆ E_{2} then

\mu(E_1) \leq \mu(E_2).
Measures of infinite unions of measurable sets
A measure μ is countably subadditive: For any countable sequence E_{1}, E_{2}, E_{3}, ... of sets E_{n} in Σ (not necessarily disjoint):

\mu\left( \bigcup_{i=1}^\infty E_i\right) \le \sum_{i=1}^\infty \mu(E_i).
A measure μ is continuous from below: If E_{1}, E_{2}, E_{3}, ... are measurable sets and E_{n} is a subset of E_{n + 1} for all n, then the union of the sets E_{n} is measurable, and

\mu\left(\bigcup_{i=1}^\infty E_i\right) = \lim_{i\to\infty} \mu(E_i).
Measures of infinite intersections of measurable sets
A measure μ is continuous from above: If E_{1}, E_{2}, E_{3}, ..., are measurable sets and for all n, E_{n + 1} ⊂ E_{n}, then the intersection of the sets E_{n} is measurable; furthermore, if at least one of the E_{n} has finite measure, then

\mu\left(\bigcap_{i=1}^\infty E_i\right) = \lim_{i\to\infty} \mu(E_i).
This property is false without the assumption that at least one of the E_{n} has finite measure. For instance, for each n ∈ N, let E_{n} = [n, ∞) ⊂ R, which all have infinite Lebesgue measure, but the intersection is empty.
Sigmafinite measures
A measure space (X, Σ, μ) is called finite if μ(X) is a finite real number (rather than ∞). Nonzero finite measures are analogous to probability measures in the sense that any finite measure μ is proportional to the probability measure \frac{1}{\mu(X)}\mu. A measure μ is called σfinite if X can be decomposed into a countable union of measurable sets of finite measure. Analogously, a set in a measure space is said to have a σfinite measure if it is a countable union of sets with finite measure.
For example, the real numbers with the standard Lebesgue measure are σfinite but not finite. Consider the closed intervals [k, k+1] for all integers k; there are countably many such intervals, each has measure 1, and their union is the entire real line. Alternatively, consider the real numbers with the counting measure, which assigns to each finite set of reals the number of points in the set. This measure space is not σfinite, because every set with finite measure contains only finitely many points, and it would take uncountably many such sets to cover the entire real line. The σfinite measure spaces have some very convenient properties; σfiniteness can be compared in this respect to the Lindelöf property of topological spaces. They can be also thought of as a vague generalization of the idea that a measure space may have 'uncountable measure'.
Completeness
A measurable set X is called a null set if μ(X) = 0. 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.
A measure can be extended to a complete one by considering the σalgebra of subsets Y which differ by a negligible set from a measurable set X, that is, such that the symmetric difference of X and Y is contained in a null set. One defines μ(Y) to equal μ(X).
Additivity
Measures are required to be countably additive. However, the condition can be strengthened as follows. For any set I and any set of nonnegative r_{i}, i\in I define:

\sum_{i\in I} r_i=\sup\left\lbrace\sum_{i\in J} r_i : J<\aleph_0, J\subseteq I\right\rbrace.
That is, we define the sum of the r_{i} to be the supremum of all the sums of finitely many of them.
A measure μ on Σ is κadditive if for any λ < κ and any family X_\alpha, α < λ the following hold:

\bigcup_{\alpha\in\lambda} X_\alpha \in \Sigma

\mu\left(\bigcup_{\alpha\in\lambda} X_\alpha\right)=\sum_{\alpha\in\lambda}\mu\left(X_\alpha\right).
Note that the second condition is equivalent to the statement that the ideal of null sets is κcomplete.
Nonmeasurable sets
If the axiom of choice is assumed to be true, not all subsets of Euclidean space are Lebesgue measurable; examples of such sets include the Vitali set, and the nonmeasurable 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 nonnegative 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. Measures that take values in Banach spaces have been studied extensively.^{[2]} A measure that takes values in the set of selfadjoint projections on a Hilbert space is called a projectionvalued measure; these are used in functional analysis for the spectral theorem. When it is necessary to distinguish the usual measures which take nonnegative 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.
Another generalization is the finitely additive measure, which are sometimes called contents. 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^{∞} and the Stone–Čech compactification. All these are linked in one way or another to the axiom of choice.
A charge is a generalization in both directions: it is a finitely additive, signed measure.
See also
References

^ Halmos, Paul (1950), Measure theory, Van Nostrand and Co.

^ Rao, M. M. (2012), Random and vector measures, Series on Multivariate Analysis 9, World Scientific Publishing Co. Pte. Ltd., Hackensack, NJ, .
Bibliography

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

Bauer, H. (2001), Measure and Integration Theory, Berlin: de Gruyter,

Bear, H.S. (2001), A Primer of Lebesgue Integration, San Diego: Academic Press,

Bogachev, V. I. (2006), Measure theory, Berlin: Springer,

Bourbaki, Nicolas (2004), Integration I, Chapter III.

R. M. Dudley, 2002. Real Analysis and Probability. Cambridge University Press.

Folland, Gerald B. (1999), Real Analysis: Modern Techniques and Their Applications, John Wiley and Sons, Second edition.

D. H. Fremlin, 2000. Measure Theory. Torres Fremlin.

Jech, Thomas (2003), Set Theory: The Third Millennium Edition, Revised and Expanded,

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

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

K. P. S. Bhaskara Rao and M. Bhaskara Rao (1983), Theory of Charges: A Study of Finitely Additive Measures, London: Academic Press, pp. x + 315,

Shilov, G. E., and Gurevich, B. L., 1978. Integral, Measure, and Derivative: A Unified Approach, Richard A. Silverman, trans. Dover Publications. ISBN 0486635198. Emphasizes the Daniell integral.


Terence Tao, 2011. An Introduction to Measure Theory. American Mathematical Society.

Nik Weaver, 2013. Measure Theory and Functional Analysis. World Scientific Publishing.
External links

Hazewinkel, Michiel, ed. (2001), "Measure",

Tutorial: Measure Theory for Dummies
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