Support (measure theory)

Support (measure theory)

In mathematics, the support (sometimes topological support or spectrum) of a measure "μ" on a measurable topological space ("X", Borel("X")) is a precise notion of where in the space "X" the measure "lives". It is defined to be the largest (closed) subset of "X" for which every open neighbourhood of every point of the set has positive measure.

Motivation

A (non-negative) measure "μ" on a measurable space ("X", Σ) is really a function "μ" : Σ → [0, +∞] . Therefore, in terms of the usual definition of support, the support of "μ" is a subset of the σ-algebra Σ:

:mathrm{supp} (mu) := overline{{ A in Sigma | mu (A) > 0 .

However, this definition is somewhat unsatisfactory: we do not even have a topology on Σ! What we really want to know is where in the space "X" the measure "μ" is non-zero. Consider two examples:
# Lebesgue measure "λ" on the real line R. It seems clear that "λ" "lives on" the whole of the real line.
# A Dirac measure "δ""p" at some point "p" ∈ R. Again, intuition suggests that the measure "δ""p" "lives at" the point "p", and nowhere else.

In light of these two examples, we can reject the following candidate definitions in favour of the one in the next section:
# We could remove the points where "μ" is zero, and take the support to be the remainder "X" { "x" ∈ "X" | "μ"({"x"}) ≠ 0 }. This might work for the Dirac measure "δ""p", but it would definitely not work for "λ": since the Lebesgue measure of any point is zero, this definition would give "λ" empty support.
# By comparison with the notion of strict positivity of measures, we could take the support to be the set of all points with a neighbourhood of positive measure:

::{ x in X | mbox{for some open } N_{x} i x, mu(N_{x}) > 0 }

:(or the closure of this). This is also too simplistic: by taking "N""x" = "X" for all points "x" ∈ "X", this would make the support of every measure except the zero measure the whole of "X".

However, the idea of "local strict positivity" is not too far from a workable definition:

Definition

Let ("X", "T") be a topological space; let Borel("X") denote the Borel σ-algebra on "X", i.e. the smallest sigma algebra on "X" that contains all open sets "U" ∈ "T". Let "μ" be a measure on ("X", Borel("X")). Then the support (or spectrum) of "μ" is defined to be the set of all points "x" in "X" for which every open neighbourhood "N""x" of "x" has positive measure:

:mathrm{supp} (mu) := { x in X | x in N_{x} in T implies mu (N_{x}) > 0 }.

Some authors prefer to take the closure of the above set. However, this is not necessary: see "Properties" below. As such, an equivalent definition of the support is as the largest closed set "C" ⊆ "X" (with respect to inclusion) such that

:U in T mbox{ and } U cap C eq varnothing implies mu (U cap C) > 0,

i.e. every open set that has non-trivial intersection with the support has positive measure.

Properties

* A measure "μ" on "X" is strictly positive if and only if it has support supp("μ") = "X". If "μ" is strictly positive and "x" ∈ "X" is arbitrary, then any open neighbourhood of "x", since it is an open set, has positive measure; hence, "x" ∈ supp("μ"), so supp("μ") = "X". Conversely, if supp("μ") = "X", then every open set (being an open neighbourhood of some point in its interior, which is also a point of the support) has positive measure; hence, "μ" is strictly positive.
* The support of a measure is closed in "X". Suppose that "x" is a limit point of supp("μ"), and let "N""x" be an open neighbourhood of "x". Since "x" is a limit point of the support, there is some "y" ∈ "N""x" ∩ supp("μ"), "y" ≠ "x". But "N""x" is also an open neighbourhood of "y", so "μ"("N""x") > 0, as required. Hence, supp("μ") contains all its limit points, i.e. it is closed.
* If "A" is a measurable set outside the support, then "A" has measure zero:::A subseteq X setminus mathrm{supp} (mu) implies mu (A) = 0.: The converse is not true in general: it fails if there exists a point "x" ∈ supp("μ") such that "μ"({"x"}) = 0 (e.g. Lebesgue measure).
* One does not need to "integrate outside the support": for any measurable function "f" : "X" → R or C,::int_{X} f(x) , mathrm{d} mu (x) = int_{mathrm{supp} (mu)} f(x) , mathrm{d} mu (x).

Examples

Lebesgue measure

In the case of Lebesgue measure "λ" on the real line R, consider an arbitrary point "x" ∈ R. Then any open neighbourhood "N""x" of "x" must contain some open interval ("x" − "ε", "x" + "ε") for some "ε" > 0. This interval has Lebesgue measure 2"ε" > 0, so "λ"("N""x") ≥ 2"ε" > 0. Since "x" ∈ R was arbitrary, supp("λ") = R.

Dirac measure

In the case of Dirac measure "δ""p", let "x" ∈ R and consider two cases:
# if "x" = "p", then every open neighbourhood "N""x" of "x" contains "p", so "δ""p"("N""x") = 1 > 0;
# on the other hand, if "x" ≠ "p", then there exists a sufficiently small open ball "B" around "x" that does not contain "p", so "δ""p"("B") = 0.We conclude that supp("δ""p") is the closure of the singleton set {"p"}, which is {"p"} itself.

In fact, a measure "μ" on the real line is a Dirac measure "δ""p" for some point "p" if and only if the support of "μ" is the singleton set {"p"}. Consequently, Dirac measure on the real line is the unique measure with zero variance [provided that the measure has variance at all] .

A uniform distribution

Consider the measure "μ" on the real line R defined by:mu (A) := lambda (A cap (0, 1))i.e. a uniform measure on the open interval (0, 1). A similar argument to the Dirac measure example shows that supp("μ") = [0, 1] . Note that the boundary points 0 and 1 lie in the support: any open set containing 0 (or 1) contains an open interval about 0 (or 1), which must intersect (0, 1), and so must have positive "μ"-measure.

igned and complex measures

Suppose that "μ" : Σ → [−∞, +∞] is a signed measure. Use the Hahn decomposition theorem to write

:mu = mu^{+} - mu^{-},

where "μ"± are both non-negative measures. Then the support of "μ" is defined to be

:mathrm{supp} (mu) := mathrm{supp} (mu^{+}) cup mathrm{supp} (mu^{-}).

Similarly, if "μ" : Σ → C is a complex measure, the support of "μ" is defined to be the union of the supports of its real and imaginary parts.

References

*
* cite book
last = Parthasarathy
first = K. R.
title = Probability measures on metric spaces
publisher = AMS Chelsea Publishing, Providence, RI
year = 2005
pages = pp.xii+276
isbn = 0-8218-3889-X
MathSciNet|id=2169627 (See chapter 2, section 2.)


Wikimedia Foundation. 2010.

Игры ⚽ Поможем сделать НИР

Look at other dictionaries:

  • Information theory and measure theory — Measures in information theory = Many of the formulas in information theory have separate versions for continuous and discrete cases, i.e. integrals for the continuous case and sums for the discrete case. These versions can often be generalized… …   Wikipedia

  • Support — may refer to the following:* Sympathy, emotional support; * Technical support (a.k.a tech support) in computer hardware, software or electronic goods; * Support (mathematics), a kind of subset of the domain of a function; * Support (measure… …   Wikipedia

  • Support (mathematics) — In mathematics, the support of a function is the set of points where the function is not zero, or the closure of that set [1]:678. This concept is used very widely in mathematical analysis. In the form of functions with support that is bounded,… …   Wikipedia

  • Theory of mind — is the ability to attribute mental states beliefs, intents, desires, pretending, knowledge, etc. to oneself and others and to understand that others have beliefs, desires and intentions that are different from one s own.[1] Though there are… …   Wikipedia

  • Theory of multiple intelligences — Human intelligence Abilities and Traits Abstract thought Communication · Creativity Emotional Intelligence Kn …   Wikipedia

  • Measure (mathematics) — 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 …   Wikipedia

  • Strictly positive measure — In mathematics, strict positivity is a concept in measure theory. Intuitively, a strictly positive measure one that is nowhere zero , or that it is zero only on points .DefinitionLet ( X , T ) be a Hausdorff topological space and let Sigma; be a… …   Wikipedia

  • Support vector machine — Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Viewing input data as two sets of vectors in an n dimensional space, an SVM will construct a separating hyperplane in that… …   Wikipedia

  • Theory of conjoint measurement — The theory of conjoint measurement (also known as conjoint measurement or additive conjoint measurement) is a general, formal theory of continuous quantity. It was independently discovered by the French economist Gerard Debreu (1960) and by the… …   Wikipedia

  • Theory of constraints — Part of a series of articles on Industry Manufacturing methods Batch production • Job production Continuous production Improvement method …   Wikipedia

Share the article and excerpts

Direct link
Do a right-click on the link above
and select “Copy Link”