Content (measure Theory)
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In mathematics, a content is a set function that is like a
measure Measure may refer to: * Measurement, the assignment of a number to a characteristic of an object or event Law * Ballot measure, proposed legislation in the United States * Church of England Measure, legislation of the Church of England * Mea ...
, but a content must only be finitely additive, whereas a measure must be countably additive. A content is a
real function In mathematical analysis, and applications in geometry, applied mathematics, engineering, and natural sciences, a function of a real variable is a function whose domain is the real numbers \mathbb, or a subset of \mathbb that contains an interv ...
\mu defined on a collection of subsets \mathcal such that # \mu(A)\in\ , \infty\text A \in \mathcal. # \mu(\varnothing) = 0. # \mu(A_1 \cup A_2) = \mu(A_1) + \mu(A_2) \text A_1, A_2, A_1\cup A_2\ \in \mathcal \text A_1 \cap A_2 = \varnothing. In many important applications the \mathcal is chosen to be a
Ring of sets In mathematics, there are two different notions of a ring of sets, both referring to certain Family of sets, families of sets. In order theory, a nonempty family of sets \mathcal is called a ring (of sets) if it is closure (mathematics), closed u ...
or to be at least a
Semiring of sets In abstract algebra, a semiring is an algebraic structure similar to a ring, but without the requirement that each element must have an additive inverse. The term rig is also used occasionally—this originated as a joke, suggesting that rigs ar ...
in which case some additional properties can be deduced which are described below. For this reason some authors prefer to define contents only for the case of semirings or even rings. If a content is additionally ''σ''-additive it is called a
pre-measure In mathematics, a pre-measure is a set function that is, in some sense, a precursor to a '' bona fide'' measure on a given space. Indeed, one of the fundamental theorems in measure theory states that a pre-measure can be extended to a measure. Def ...
and if furthermore \mathcal is a ''σ''-algebra, the content is called a
measure Measure may refer to: * Measurement, the assignment of a number to a characteristic of an object or event Law * Ballot measure, proposed legislation in the United States * Church of England Measure, legislation of the Church of England * Mea ...
. Therefore every (real-valued) measure is a content, but not vice versa. Contents give a good notion of integrating bounded functions on a space but can behave badly when integrating unbounded functions, while measures give a good notion of integrating unbounded functions.


Examples

A classical example is to define a content on all half open intervals [a,b) \subseteq \R by setting their content to the length of the intervals, that is, \mu([a,b))=b-a. One can further show that this content is actually ''σ''-additive and thus defines a pre-measure on the semiring of all half-open intervals. This can be used to construct the Lebesgue measure for the real number line using Carathéodory's extension theorem. For further details on the general construction see article on Lebesgue measure#Construction of the Lebesgue measure, Lebesgue measure. An example of a content that is not a measure on a ''σ''-algebra is the content on all subsets of the positive integers that has value 1/2^n on any integer n and is infinite on any infinite subset. An example of a content on the positive integers that is always finite but is not a measure can be given as follows. Take a positive linear functional on the bounded sequences that is 0 if the sequence has only a finite number of nonzero elements and takes value 1 on the sequence 1, 1, 1, \ldots, so the functional in some sense gives an "average value" of any bounded sequence. (Such a functional cannot be constructed explicitly, but exists by the
Hahn–Banach theorem The Hahn–Banach theorem is a central tool in functional analysis. It allows the extension of bounded linear functionals defined on a subspace of some vector space to the whole space, and it also shows that there are "enough" continuous linear f ...
.) Then the content of a set of positive integers is the average value of the sequence that is 1 on this set and 0 elsewhere. Informally, one can think of the content of a subset of integers as the "chance" that a randomly chosen integer lies in this subset (though this is not compatible with the usual definitions of chance in probability theory, which assume countable additivity).


Properties

Frequently contents are defined on collections of sets that satisfy further constraints. In this case additional properties can be deduced that fail to hold in general for contents defined on any collections of sets.


On semirings

If \mathcal forms a
Semiring of sets In abstract algebra, a semiring is an algebraic structure similar to a ring, but without the requirement that each element must have an additive inverse. The term rig is also used occasionally—this originated as a joke, suggesting that rigs ar ...
then the following statements can be deduced: * Every content \mu is ''monotone'' that is, A \subseteq B \Rightarrow \mu(A) \leq \mu(B) \text A, B \in \mathcal. * Every content \mu is ''subadditive'' that is, :\mu(A \cup B) \leq \mu(A) + \mu(B) for A, B \in \mathcal such that A \cup B \in \mathcal.


On rings

If furthermore \mathcal is a
Ring of sets In mathematics, there are two different notions of a ring of sets, both referring to certain Family of sets, families of sets. In order theory, a nonempty family of sets \mathcal is called a ring (of sets) if it is closure (mathematics), closed u ...
one gets additionally: * ''Subtractivity'': for B \subseteq A satisfying \mu (B) < \infty it follows \mu (A \setminus B) = \mu (A) - \mu (B). * A,B\in\mathcal \Rightarrow \mu(A\cup B)+\mu(A\cap B) = \mu(A)+\mu(B). * ''Subadditivity'': A_i\in \mathcal\; (i=1,2,\dotsc,n) \Rightarrow \mu\left(\bigcup_^n A_i\right)\leq \sum_^n \mu(A_i). * ''\sigma-Superadditivity'': For any A_i \in \mathcal\; (i=1,2,\dotsc)\ pairwise disjoint satisfying \bigcup_^\infty A_i\in \mathcal we have \mu\left(\bigcup_^\infty A_i\right) \geq \sum_^\infty \mu(A_i). * If \mu is a finite content, that is, A \in\mathcal \Rightarrow \mu(A)<\infty, then the
inclusion–exclusion principle In combinatorics, a branch of mathematics, the inclusion–exclusion principle is a counting technique which generalizes the familiar method of obtaining the number of elements in the union of two finite sets; symbolically expressed as : , A \cu ...
applies: \mu\left(\bigcup_^nA_i\right) = \sum_^n(-1)^\!\!\sum_\!\!\!\!\mu\left(\bigcap_A_i\right) where A_i\in \mathcal for all i\in\.


Integration of bounded functions

In general integration of functions with respect to a content does not behave well. However there is a well-behaved notion of integration provided that the function is bounded and the total content of the space is finite, given as follows. Suppose that the total content of a space is finite. If f is a bounded function on the space such that the inverse image of any open subset of the reals has a content, then we can define the integral of f with respect to the content as \int f \, d\lambda = \lim \sum_^n f(\alpha_i)\lambda (f^(A_i)) where the A_i form a finite collections of disjoint half-open sets whose union covers the range of f, and \alpha_i is any element of A_i, and where the limit is taken as the diameters of the sets A_i tend to 0.


Duals of spaces of bounded functions

Suppose that \mu is a measure on some space X. The bounded measurable functions on X form a Banach space with respect to the supremum norm. The positive elements of the dual of this space correspond to bounded contents \lambda X, with the value of \lambda on f given by the integral \int f \, d\lambda. Similarly one can form the space of essentially bounded functions, with the norm given by the essential supremum, and the positive elements of the dual of this space are given by bounded contents that vanish on sets of measure 0.


Construction of a measure from a content

There are several ways to construct a measure μ from a content \lambda on a topological space. This section gives one such method for locally compact
Hausdorff space In topology and related branches of mathematics, a Hausdorff space ( , ), separated space or T2 space is a topological space where, for any two distinct points, there exist neighbourhoods of each which are disjoint from each other. Of the m ...
s such that the content is defined on all compact subsets. In general the measure is not an extension of the content, as the content may fail to be countably additive, and the measure may even be identically zero even if the content is not. First restrict the content to compact sets. This gives a function \lambda of compact sets C with the following properties: # \lambda(C) \in\ , \infty/math> for all compact sets C # \lambda(\varnothing) = 0. # \lambda(C_1) \leq \lambda(C_2) \text C_1\subseteq C_2 # \lambda(C_1 \cup C_2) \leq \lambda(C_1) + \lambda(C_2) for all pairs of compact sets # \lambda(C_1 \cup C_2) = \lambda(C_1) + \lambda(C_2) for all pairs of disjoint compact sets. There are also examples of functions \lambda as above not constructed from contents. An example is given by the construction of Haar measure on a
locally compact group In mathematics, a locally compact group is a topological group ''G'' for which the underlying topology is locally compact and Hausdorff. Locally compact groups are important because many examples of groups that arise throughout mathematics are loc ...
. One method of constructing such a Haar measure is to produce a left-invariant function \lambda as above on the compact subsets of the group, which can then be extended to a left-invariant measure.


Definition on open sets

Given λ as above, we define a function μ on all open sets by \mu(U) = \sup_ \lambda (C). This has the following properties: # \mu(U) \in\ , \infty/math> # \mu(\varnothing) = 0 # \mu(U_1) \leq \mu(U_2) \text U_1\subseteq U_2 # \mu\left(\bigcup_nU_n\right) \leq \sum_n\lambda(U_n) for any collection of open sets # \mu\left(\bigcup_nU_n\right) = \sum_n\lambda(U_n) for any collection of disjoint open sets.


Definition on all sets

Given μ as above, we extend the function μ to all subsets of the topological space by \mu(A) = \inf_\mu (U). This is an
outer measure In the mathematical field of measure theory, an outer measure or exterior measure is a function defined on all subsets of a given set with values in the extended real numbers satisfying some additional technical conditions. The theory of outer mea ...
, in other words it has the following properties: # \mu(A) \in\ , \infty/math> # \mu(\varnothing) = 0. # \mu(A_1) \leq \mu(A_2) \text A_1\subseteq A_2 # \mu\left(\bigcup_nA_n\right) \leq \sum_n\lambda(A_n) for any countable collection of sets.


Construction of a measure

The function μ above is an
outer measure In the mathematical field of measure theory, an outer measure or exterior measure is a function defined on all subsets of a given set with values in the extended real numbers satisfying some additional technical conditions. The theory of outer mea ...
on the family of all subsets. Therefore it becomes a measure when restricted to the measurable subsets for the outer measure, which are the subsets E such that \mu(X) = \mu(X \cap E) + \mu(X \setminus E) for all subsets X. If the space is locally compact then every open set is measurable for this measure. The measure \mu does not necessarily coincide with the content \lambda on compact sets, However it does if \lambda is regular in the sense that for any compact C, \lambda(C) is the inf of \lambda(D) for compact sets D containing C in their interiors.


See also

*


References

* * * {{Measure theory Measure theory Families of sets