Set function

Short description: Function from sets to numbers

In mathematics, especially measure theory, a set function is a function whose domain is a family of subsets of some given set and that (usually) takes its values in the extended real number line ℝ∪{±∞}, which consists of the real numbers ℝ and ±∞.

A set function generally aims to measure subsets in some way. Measures are typical examples of "measuring" set functions. Therefore, the term "set function" is often used for avoiding confusion between the mathematical meaning of "measure" and its common language meaning.

## Contents

* 1 Definitions
* 1.1 Common properties of set functions
* 1.2 Inner measures, outer measures, and other properties
* 1.3 Topology related definitions
* 1.4 Relationships between set functions
* 2 Examples
* 2.1 Lebesgue measure
* 2.1.1 Infinite-dimensional space
* 2.2 Finitely additive translation-invariant set functions
* 3 Extending set functions
* 3.1 Extending from semialgebras to algebras
* 3.2 Extending from rings to σ-algebras
* 3.3 Restricting outer measures
* 4 See also
* 5 Notes
* 6 References
* 7 Further reading

## Definitions

If ℱ is a family of sets over Ω (meaning that ℱ⊆℘(Ω) where ℘(Ω) denotes the powerset) then a set function on ℱ is a function μ with domain ℱ and codomain [−∞,∞] or, sometimes, the codomain is instead some vector space, as with vector measures, complex measures, and projection-valued measures. The domain of a set function may have any number properties; the commonly encountered properties and categories of families are listed in the table below. Template:Families of sets

In general, it is typically assumed that μ(E)+μ(F) is always well-defined for all E,F∈ℱ, or equivalently, that μ does not take on both −∞ and +∞ as values. This article will henceforth assume this; although alternatively, all definitions below could instead be qualified by statements such as "whenever the sum/series is defined". This is sometimes done with subtraction, such as with the following result, which holds whenever μ is finitely additive:

Set difference formula: μ(F)−μ(E)=μ(F∖E) whenever μ(F)−μ(E) is defined with E,F∈ℱ satisfying E⊆F and F∖E∈ℱ.

Null sets

A set F∈ℱ is called a null set (with respect to μ) or simply null if μ(F)=0. Whenever μ is not identically equal to either −∞ or +∞ then it is typically also assumed that:

* null empty set: μ(∅)=0 if ∅∈ℱ.

Variation and mass

The total variation of a set S is |μ|(S)=defsup⁡{|μ(F)|:F∈ℱ and F⊆S} where |⋅| denotes the absolute value (or more generally, it denotes the norm or seminorm if μ is vector-valued in a (semi)normed space). Assuming that ∪ℱ=def⋃F∈ℱF∈ℱ, then |μ|(∪ℱ) is called the total variation of μ and μ(∪ℱ) is called the mass of μ.

A set function is called finite if for every F∈ℱ, the value μ(F) is finite (which by definition means that μ(F)≠∞ and μ(F)≠−∞; an infinite value is one that is equal to ∞ or −∞). Every finite set function must have a finite mass.

### Common properties of set functions

A set function μ on ℱ is said to be[1]

* non-negative if it is valued in [0,∞].
* finitely additive if ∑i=1nμ(Fi)=μ(⋃i=1nFi) for all pairwise disjoint finite sequences F1,…,Fn∈ℱ such that ⋃i=1nFi∈ℱ.
* If ℱ is closed under binary unions then μ is finitely additive if and only if μ(E∪F)=μ(E)+μ(F) for all disjoint pairs E,F∈ℱ.
* If μ is finitely additive and if ∅∈ℱ then taking E:=F:=∅ shows that μ(∅)=μ(∅)+μ(∅) which is only possible if μ(∅)=0 or μ(∅)=±∞, where in the latter case, μ(E)=μ(E∪∅)=μ(E)+μ(∅)=μ(E)+(±∞)=±∞ for every E∈ℱ (so only the case μ(∅)=0 is useful).
* countably additive or σ-additive[2] if in addition to being finitely additive, for all pairwise disjoint sequences F1,F2,… in ℱ such that ⋃i=1∞Fi∈ℱ, all of the following hold:
1. ∑i=1∞μ(Fi)=μ(⋃i=1∞Fi)
* The series on the left hand side is defined in the usual way as the limit ∑i=1∞μ(Fi)=deflimn->∞μ(F1)+⋯+μ(Fn).
* As a consequence, if ρ:ℕ->ℕ is any permutation/bijection then ∑i=1∞μ(Fi)=∑i=1∞μ(Fρ(i)); this is because ⋃i=1∞Fi=⋃i=1∞Fρ(i) and applying this condition (a) twice guarantees that both ∑i=1∞μ(Fi)=μ(⋃i=1∞Fi) and μ(⋃i=1∞Fρ(i))=∑i=1∞μ(Fρ(i)) hold. By definition, a convergent series with this property is said to be unconditionally convergent. Stated in plain English, this means that rearranging/relabeling the sets F1,F2,… to the new order Fρ(1),Fρ(2),… does not affect the sum of their measures. This is desirable since just as the union F=def⋃i∈ℕFi does not depend on the order of these sets, the same should be true of the sums μ(F)=μ(F1)+μ(F2)+⋯ and μ(F)=μ(Fρ(1))+μ(Fρ(2))+⋯.
2. if μ(⋃i=1∞Fi) is not infinite then this series ∑i=1∞μ(Fi) must also converge absolutely, which by definition means that ∑i=1∞|μ(Fi)| must be finite. This is automatically true if μ is non-negative (or even just valued in the extended real numbers).
* As with any convergent series of real numbers, by the Riemann series theorem, the series ∑i=1∞μ(Fi)=limN->∞μ(F1)+μ(F2)+⋯+μ(FN) converges absolutely if and only if its sum does not depend on the order of its terms (a property known as unconditional convergence). Since unconditional convergence is guaranteed by (a) above, this condition is automatically true if μ is valued in [−∞,∞].
3. if μ(⋃i=1∞Fi)=∑i=1∞μ(Fi) is infinite then it is also required that the value of at least one of the series ∑μ(Fi)>0i∈ℕμ(Fi) and ∑μ(Fi)<0i∈ℕμ(Fi) be finite (so that the sum of their values is well-defined). This is automatically true if μ is non-negative.
* a pre-measure if it is non-negative, countably additive (including finitely additive), and has a null empty set.
* a measure if it is a pre-measure whose domain is a σ-algebra. That is to say, a measure is a non-negative countably additive set function on a σ-algebra that has a null empty set.
* a probability measure if it is a measure that has a mass of 1.
* an outer measure if it is non-negative, countably subadditive, has a null empty set, and has the power set ℘(Ω) as its domain.
* Outer measures appear in the Carathéodory's extension theorem and they are often restricted to Carathéodory measurable subsets
* a signed measure if it is countably additive, has a null empty set, and μ does not take on both −∞ and +∞ as values.
* complete if every subset of every null set is null; explicitly, this means: whenever F∈ℱ satisfies μ(F)=0 and N⊆F is any subset of F then N∈ℱ and μ(N)=0.
* Unlike many other properties, completeness places requirements on the set domain⁡μ=ℱ (and not just on μ's values).
* 𝜎-finite if there exists a sequence F1,F2,F3,… in ℱ such that μ(Fi) is finite for every index i, and also ⋃n=1∞Fn=⋃F∈ℱF.
* decomposable if there exists a subfamily 𝒫⊆ℱ of pairwise disjoint sets such that μ(P) is finite for every P∈𝒫 and also ⋃P∈𝒫P=⋃F∈ℱF (where ℱ=domain⁡μ).
* Every 𝜎-finite set function is decomposable although not conversely. For example, the counting measure on ℝ (whose domain is ℘(ℝ)) is decomposable but not 𝜎-finite.
* a vector measure if it is a countably additive set function μ:ℱ->X valued in a topological vector space X (such as a normed space) whose domain is a σ-algebra.
* If μ is valued in a normed space (X,‖⋅‖) then it is countably additive if and only if for any pairwise disjoint sequence F1,F2,… in ℱ, limn->∞‖μ(F1)+⋯+μ(Fn)−μ(⋃i=1∞Fi)‖=0. If μ is finitely additive and valued in a Banach space then it is countably additive if and only if for any pairwise disjoint sequence F1,F2,… in ℱ, limn->∞‖μ(Fn∪Fn+1∪Fn+2∪⋯)‖=0.
* a complex measure if it is a countably additive complex-valued set function μ:ℱ->ℂ whose domain is a σ-algebra.
* By definition, a complex measure never takes ±∞ as a value and so has a null empty set.
* a random measure if it is a measure-valued random element.

Arbitrary sums

As described in this article's section on generalized series, for any family (ri)i∈I of real numbers indexed by an arbitrary indexing set I, it is possible to define their sum ∑i∈Iri as the limit of the net of finite partial sums F∈FiniteSubsets⁡(I)↦∑i∈Fri where the domain FiniteSubsets⁡(I) is directed by ⊆. Whenever this net converges then its limit is denoted by the symbols ∑i∈Iri while if this net instead diverges to ±∞ then this may be indicated by writing ∑i∈Iri=±∞. Any sum over the empty set is defined to be zero; that is, if I=∅ then ∑i∈∅ri=0 by definition.

For example, if zi=0 for every i∈I then ∑i∈Izi=0. And it can be shown that ∑i∈Iri=∑ri=0i∈I,ri+∑ri≠0i∈I,ri=0+∑ri≠0i∈I,ri=∑ri≠0i∈I,ri. If I=ℕ then the generalized series ∑i∈Iri converges in ℝ if and only if ∑i=1∞ri converges unconditionally (or equivalently, converges absolutely) in the usual sense. If a generalized series ∑i∈Iri converges in ℝ then both ∑ri>0i∈Iri and ∑ri<0i∈Iri also converge to elements of ℝ and the set {i∈I:ri≠0} is necessarily countable (that is, either finite or countably infinite); this remains true if ℝ is replaced with any normed space.[proof 1] It follows that in order for a generalized series ∑i∈Iri to converge in ℝ or ℂ, it is necessary that all but at most countably many ri will be equal to 0, which means that ∑i∈Iri=∑ri≠0i∈Iri is a sum of at most countably many non-zero terms. Said differently, if {i∈I:ri≠0} is uncountable then the generalized series ∑i∈Iri does not converge.

In summary, due to the nature of the real numbers and its topology, every generalized series of real numbers (indexed by an arbitrary set) that converges can be reduced to an ordinary absolutely convergent series of countably many real numbers. So in the context of measure theory, there is little benefit gained by considering uncountably many sets and generalized series. In particular, this is why the definition of "countably additive" is rarely extended from countably many sets F1,F2,… in ℱ (and the usual countable series ∑i=1∞μ(Fi)) to arbitrarily many sets (Fi)i∈I (and the generalized series ∑i∈Iμ(Fi)).

### Inner measures, outer measures, and other properties

A set function μ is said to be/satisfies[1]

* monotone if μ(E)≤μ(F) whenever E,F∈ℱ satisfy E⊆F.
* modular if it satisfies the following condition, known as modularity: μ(E∪F)+μ(E∩F)=μ(E)+μ(F) for all E,F∈ℱ such that E∪F,E∩F∈ℱ.
* Every finitely additive function on a field of sets is modular.
* In geometry, a set function valued in some abelian semigroup that possess this property is known as a valuation. This geometric definition of "valuation" should not be confused with the stronger non-equivalent measure theoretic definition of "valuation" that is given below.
* submodular if μ(E∪F)+μ(E∩F)≤μ(E)+μ(F) for all E,F∈ℱ such that E∪F,E∩F∈ℱ.
* finitely subadditive if |μ(F)|≤∑i=1n|μ(Fi)| for all finite sequences F,F1,…,Fn∈ℱ that satisfy F⊆⋃i=1nFi.
* countably subadditive or σ-subadditive if |μ(F)|≤∑i=1∞|μ(Fi)| for all sequences F,F1,F2,F3,… in ℱ that satisfy F⊆⋃i=1∞Fi.
* If ℱ is closed under finite unions then this condition holds if and only if |μ(F∪G)|≤|μ(F)|+|μ(G)| for all F,G∈ℱ. If μ is non-negative then the absolute values may be removed.
* If μ is a measure then this condition holds if and only if μ(⋃i=1∞Fi)≤∑i=1∞μ(Fi) for all F1,F2,F3,… in ℱ.[3] If μ is a probability measure then this inequality is Boole's inequality.
* If μ is countably subadditive and ∅∈ℱ with μ(∅)=0 then μ is finitely subadditive.
* superadditive if μ(E)+μ(F)≤μ(E∪F) whenever E,F∈ℱ are disjoint with E∪F∈ℱ.
* continuous from above if limn->∞μ(Fi)=μ(⋂i=1∞Fi) for all non-increasing sequences of sets F1⊇F2⊇F3⋯ in ℱ such that ⋂i=1∞Fi∈ℱ with μ(⋂i=1∞Fi) and all μ(Fi) finite.
* Lebesgue measure λ is continuous from above but it would not be if the assumption that all μ(Fi) are eventually finite was omitted from the definition, as this example shows: For every integer i, let Fi be the open interval (i,∞) so that limn->∞λ(Fi)=limn->∞∞=∞≠0=λ(∅)=λ(⋂i=1∞Fi) where ⋂i=1∞Fi=∅.
* continuous from below if limn->∞μ(Fi)=μ(⋃i=1∞Fi) for all non-decreasing sequences of sets F1⊆F2⊆F3⋯ in ℱ such that ⋃i=1∞Fi∈ℱ.
* infinity is approached from below if whenever F∈ℱ satisfies μ(F)=∞ then for every real r>0, there exists some Fr∈ℱ such that Fr⊆F and r≤μ(Fr)<∞.
* an outer measure if μ is non-negative, countably subadditive, has a null empty set, and has the power set ℘(Ω) as its domain.
* an inner measure if μ is non-negative, superadditive, continuous from above, has a null empty set, has the power set ℘(Ω) as its domain, and +∞ is approached from below.
* atomic if every measurable set of positive measure contains an atom.

If a binary operation + is defined, then a set function μ is said to be

* translation invariant if μ(ω+F)=μ(F) for all ω∈Ω and F∈ℱ such that ω+F∈ℱ.

### Topology related definitions

If τ is a topology on Ω then a set function μ is said to be:

* a Borel measure if it is a measure defined on the σ-algebra of all Borel sets, which is the smallest σ-algebra containing all open subsets (that is, containing τ).
* a Baire measure if it is a measure defined on the σ-algebra of all Baire sets.
* locally finite if for every point ω∈Ω there exists some neighborhood U∈ℱ∩τ of this point such that μ(U) is finite.
* If μ is a finitely additive, monotone, and locally finite then μ(K) is necessarily finite for every compact measurable subset K.
* τ-additive if μ(⋃𝒟)=supD∈𝒟μ(D) whenever 𝒟⊆τ∩ℱ is directed with respect to ⊆ and satisfies ⋃𝒟=def⋃D∈𝒟D∈ℱ.
* 𝒟 is directed with respect to ⊆ if and only if it is not empty and for all A,B∈𝒟 there exists some C∈𝒟 such that A⊆C and B⊆C.
* inner regular or tight if for every F∈ℱ, μ(F)=sup⁡{μ(K):F⊇K with K∈ℱ a compact subset of (Ω,τ)}.
* outer regular if for every F∈ℱ, μ(F)=inf⁡{μ(U):F⊆U and U∈ℱ∩τ}.
* regular if it is both inner regular and outer regular.
* a Borel regular measure if it is a Borel measure that is also regular.
* a Radon measure if it is a regular and locally finite measure.
* strictly positive if every non-empty open subset has (strictly) positive measure.
* a valuation if it is non-negative, monotone, modular, has a null empty set, and has domain τ.

### Relationships between set functions

See also: Radon–Nikodym theorem and Lebesgue's decomposition theorem

If μ and ν are two set functions over Ω, then:

* μ is said to be absolutely continuous with respect to ν or dominated by ν, written μ≪ν, if for every set F that belongs to the domain of both μ and ν, if ν(F)=0 then μ(F)=0.
* If μ and ν are σ-finite measures on the same measurable space and if μ≪ν, then the Radon–Nikodym derivative dμdν exists and for every measurable F, μ(F)=∫Fdμdνdν.
* μ and ν are called equivalent if each one is absolutely continuous with respect to the other. μ is called a supporting measure of a measure ν if μ is σ-finite and they are equivalent.[4]
* μ and ν are singular, written μ⊥ν, if there exist disjoint sets M and N in the domains of μ and ν such that M∪N=Ω, μ(F)=0 for all F⊆M in the domain of μ, and ν(F)=0 for all F⊆N in the domain of ν.

## Examples

Examples of set functions include:

* The function d(A)=limn->∞|A∩{1,…,n}|n, assigning densities to sufficiently well-behaved subsets A⊆{1,2,3,…}, is a set function.
* A probability measure assigns a probability to each set in a σ-algebra. Specifically, the probability of the empty set is zero and the probability of the sample space is 1, with other sets given probabilities between 0 and 1.
* A possibility measure assigns a number between zero and one to each set in the powerset of some given set. See possibility theory.
* A random set is a set-valued random variable. See the article random compact set.

The Jordan measure on ℝn is a set function defined on the set of all Jordan measurable subsets of ℝn; it sends a Jordan measurable set to its Jordan measure.

### Lebesgue measure

The Lebesgue measure on ℝ is a set function that assigns a non-negative real number to every set of real numbers that belongs to the Lebesgue σ-algebra.[5]

Its definition begins with the set Intervals⁡(ℝ) of all intervals of real numbers, which is a semialgebra on ℝ. The function that assigns to every interval I its length⁡(I) is a finitely additive set function (explicitly, if I has endpoints a≤b then length⁡(I)=b−a). This set function can be extended to the Lebesgue outer measure on ℝ, which is the translation-invariant set function λ*:℘(ℝ)->[0,∞] that sends a subset E⊆ℝ to the infimum λ*(E)=inf⁡{∑k=1∞length⁡(Ik):(Ik)k∈ℕ is a sequence of open intervals with E⊆⋃k=1∞Ik}. Lebesgue outer measure is not countably additive (and so is not a measure) although its restriction to the 𝜎-algebra of all subsets M⊆ℝ that satisfy the Carathéodory criterion: λ*(M)=λ*(M∩E)+λ*(M∩Ec) for every S⊆ℝ is a measure that called Lebesgue measure. Vitali sets are examples of non-measurable sets of real numbers.

#### Infinite-dimensional space

As detailed in the article on infinite-dimensional Lebesgue measure, the only locally finite and translation-invariant Borel measure on an infinite-dimensional separable normed space is the trivial measure. However, it is possible to define Gaussian measures on infinite-dimensional topological vector spaces. The structure theorem for Gaussian measures shows that the abstract Wiener space construction is essentially the only way to obtain a strictly positive Gaussian measure on a separable Banach space.

### Finitely additive translation-invariant set functions

The only translation-invariant measure on Ω=ℝ with domain ℘(ℝ) that is finite on every compact subset of ℝ is the trivial set function ℘(ℝ)->[0,∞] that is identically equal to 0 (that is, it sends every S⊆ℝ to 0)[6] However, if countable additivity is weakened to finite additivity then a non-trivial set function with these properties does exist and moreover, some are even valued in [0,1]. In fact, such non-trivial set functions will exist even if ℝ is replaced by any other abelian group G.[7]

Theorem[8] -- If (G,+) is any abelian group then there exists a finitely additive and translation-invariant[note 1] set function μ:℘(G)->[0,1] of mass μ(G)=1.

## Extending set functions

See also: Carathéodory's extension theorem

### Extending from semialgebras to algebras

Suppose that μ is a set function on a semialgebra ℱ over Ω and let algebra⁡(ℱ):={F1⊔⋯⊔Fn:n∈ℕ and F1,…,Fn∈ℱ are pairwise disjoint }, which is the algebra on Ω generated by ℱ. The archetypal example of a semialgebra that is not also an algebra is the family 𝒮d:={∅}∪{(a1,b1]×⋯×(a1,b1]:−∞≤ai<bi≤∞ for all i=1,…,d} on Ω:=ℝd where (a,b]:={x∈ℝ:a<x≤b} for all −∞≤a<b≤∞.[9] Importantly, the two non-strict inequalities ≤ in −∞≤ai<bi≤∞ cannot be replaced with strict inequalities < since semialgebras must contain the whole underlying set ℝd; that is, ℝd∈𝒮d is a requirement of semialgebras (as is ∅∈𝒮d).

If μ is finitely additive then it has a unique extension to a set function μ‾ on algebra⁡(ℱ) defined by sending F1⊔⋯⊔Fn∈algebra⁡(ℱ) (where ⊔ indicates that these Fi∈ℱ are pairwise disjoint) to:[9] μ‾(F1⊔⋯⊔Fn):=μ(F1)+⋯+μ(Fn). This extension μ‾ will also be finitely additive: for any pairwise disjoint A1,…,An∈algebra⁡(ℱ), [9] μ‾(A1∪⋯∪An)=μ‾(A1)+⋯+μ‾(An).

If in addition μ is extended real-valued and monotone (which, in particular, will be the case if μ is non-negative) then μ‾ will be monotone and finitely subadditive: for any A,A1,…,An∈algebra⁡(ℱ) such that A⊆A1∪⋯∪An,[9] μ‾(A)≤μ‾(A1)+⋯+μ‾(An).

### Extending from rings to σ-algebras

See also: Pre-measure and Hahn–Kolmogorov theorem

If μ:ℱ->[0,∞] is a pre-measure on a ring of sets (such as an algebra of sets) ℱ over Ω then μ has an extension to a measure μ‾:σ(ℱ)->[0,∞] on the σ-algebra σ(ℱ) generated by ℱ. If μ is σ-finite then this extension is unique.

To define this extension, first extend μ to an outer measure μ* on 2Ω=℘(Ω) by μ*(T)=inf⁡{∑nμ(Sn):T⊆∪nSn with S1,S2,…∈ℱ} and then restrict it to the set ℱM of μ*-measurable sets (that is, Carathéodory-measurable sets), which is the set of all M⊆Ω such that μ*(S)=μ*(S∩M)+μ*(S∩Mc) for every subset S⊆Ω. It is a σ-algebra and μ* is sigma-additive on it, by Caratheodory lemma.

### Restricting outer measures

If μ*:℘(Ω)->[0,∞] is an outer measure on a set Ω, where (by definition) the domain is necessarily the power set ℘(Ω) of Ω, then a subset M⊆Ω is called μ*–measurable or Carathéodory-measurable if it satisfies the following Carathéodory's criterion: μ*(S)=μ*(S∩M)+μ*(S∩Mc) for every subset S⊆Ω, where Mc:=Ω∖M is the complement of M.

The family of all μ*–measurable subsets is a σ-algebra and the restriction of the outer measure μ* to this family is a measure.

## See also

* Boolean ring - Algebraic structure in mathematics
* Cylinder set measure
* Field of sets - Algebraic concept in measure theory, also referred to as an algebra of sets
* Hadwiger's theorem
* Hahn decomposition theorem - Measurability theorem
* Invariant measure
* Lebesgue's decomposition theorem - Theorem
* Positive and negative sets
* Radon–Nikodym theorem - Expressing a measure as an integral of another
* Riesz–Markov–Kakutani representation theorem - Statement about linear functionals and measures
* Ring of sets - Family closed under unions and relative complements
* Σ-algebra
* Vitali–Hahn–Saks theorem

## Notes

1. ↑ 1.0 1.1 Durrett 2019, pp. 1–37, 455–470.
2. ↑ Durrett 2019, pp. 466–470.
3. ↑ Royden & Fitzpatrick 2010, p. 30.
4. ↑ Kallenberg, Olav (2017). Random Measures, Theory and Applications. Probability Theory and Stochastic Modelling. 77. Switzerland: Springer. p. 21. doi:10.1007/978-3-319-41598-7. ISBN 978-3-319-41596-3.
5. ↑ Kolmogorov and Fomin 1975
6. ↑ Rudin 1991, p. 139.
7. ↑ Rudin 1991, pp. 139–140.
8. ↑ Rudin 1991, pp. 141–142.
9. ↑ 9.0 9.1 9.2 9.3 Durrett 2019, pp. 1–9.

1. ↑ The function μ being translation-invariant means that μ(S)=μ(g+S) for every g∈G and every subset S⊆G.

Proofs

1. ↑ Suppose the net ∑i∈Iri=deflim⁡A∈Finite⁡(I) ∑i∈Ari=lim⁡{∑i∈Ari:A⊆I,A finite } converges to some point in a metrizable topological vector space X (such as ℝ, ℂ, or a normed space), where recall that this net's domain is the directed set (Finite⁡(I),⊆). Like every convergent net, this convergent net of partial sums A↦∑i∈Ari is a Cauchy net, which for this particular net means (by definition) that for every neighborhood W of the origin in X, there exists a finite subset A0 of I such that ∑i∈Bri−∑i∈Cri∈W for all finite supersets B,C⊇A0; this implies that ri∈W for every i∈I∖A0 (by taking B:=A0∪{i} and C:=A0). Since X is metrizable, it has a countable neighborhood basis U1,U2,… at the origin, whose intersection is necessarily U1∩U2∩⋯={0} (since X is a Hausdorff TVS). For every positive integer n∈ℕ, pick a finite subset An⊆I such that ri∈Un for every i∈I∖An. If i belongs to (I∖A1)∩(I∖A2)∩⋯=I∖(A1∪A2∪⋯) then ri belongs to U1∩U2∩⋯={0}. Thus ri=0 for every index i∈I that does not belong to the countable set A1∪A2∪⋯. ◼

## References

* Template:Durrett Probability Theory and Examples 5th Edition
* Template:Kolmogorov Fomin Elements of the Theory of Functions and Functional Analysis
* A. N. Kolmogorov and S. V. Fomin (1975), Introductory Real Analysis, Dover. ISBN 0-486-61226-0
* Template:Royden Fitzpatrick Real Analysis 4th 2010
* Rudin, Walter (January 1, 1991). Functional Analysis. International Series in Pure and Applied Mathematics. 8 (Second ed.). New York, NY: McGraw-Hill Science/Engineering/Math. ISBN 978-0-07-054236-5. OCLC 21163277. https://archive.org/details/functionalanalys00rudi.

## Further reading

* Hazewinkel, Michiel, ed. (2001), "Set function", Encyclopedia of Mathematics, Springer Science+Business Media B.V. / Kluwer Academic Publishers, ISBN 978-1-55608-010-4, https://www.encyclopediaofmath.org/index.php?title=S/s084730
* Regular set function at Encyclopedia of Mathematics

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* Cylinder set
* 𝜆-system
* Essential range
* infimum/supremum
* Locally measurable
* π-system
* σ-algebra
* Non-measurable set
* Vitali set
* Null set
* Support
* Transverse measure
* Universally measurable

Types of measures|

* Atomic
* Baire
* Banach
* Besov
* Borel
* Brown
* Complex
* Complete
* Content
* (Logarithmically) Convex
* Decomposable
* Discrete
* Equivalent
* Finite
* Inner
* (Quasi-) Invariant
* Locally finite
* Maximising
* Metric outer
* Outer
* Perfect
* Pre-measure
* (Sub-) Probability
* Projection-valued
* Radon
* Random
* Regular
* Borel regular
* Inner regular
* Outer regular
* Saturated
* Set function
* σ-finite
* s-finite
* Signed
* Singular
* Spectral
* Strictly positive
* Tight
* Vector

Particular measures|

* Counting
* Dirac
* Euler
* Gaussian
* Haar
* Harmonic
* Hausdorff
* Intensity
* Lebesgue
* Infinite-dimensional
* Logarithmic
* Product
* Projections
* Pushforward
* Spherical measure
* Tangent
* Trivial
* Young

Maps|

* Measurable function
* Bochner
* Strongly
* Weakly
* Convergence: almost everywhere
* of measures
* in measure
* of random variables
* in distribution
* in probability
* Cylinder set measure
* Random: compact set
* element
* measure
* process
* variable
* vector
* Projection-valued measure

Main results|

* Carathéodory's extension theorem
* Convergence theorems
* Dominated
* Monotone
* Vitali
* Decomposition theorems
* Hahn
* Jordan
* Maharam's
* Egorov's
* Fatou's lemma
* Fubini's
* Fubini–Tonelli
* Hölder's inequality
* Minkowski inequality
* Radon–Nikodym
* Riesz–Markov–Kakutani representation theorem

Other results|

* Disintegration theorem
* Lifting theory
* Lebesgue's density theorem
* Lebesgue differentiation theorem
* Sard's theorem
* Vitali–Hahn–Saks theorem

For Lebesgue measure|

* Isoperimetric inequality
* Brunn–Minkowski theorem
* Milman's reverse
* Minkowski–Steiner formula
* Prékopa–Leindler inequality
* Vitale's random Brunn–Minkowski inequality

Applications & related|

* Convex analysis
* Descriptive set theory
* Probability theory
* Real analysis
* Spectral theory

Template:Analysis in topological vector spaces

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