Measure

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\newcommand{\bigudot}{ \mathchoice{\mathop{\bigcup\mkern-15mu\cdot\mkern8mu}}{\mathop{\bigcup\mkern-13mu\cdot\mkern5mu}}{\mathop{\bigcup\mkern-13mu\cdot\mkern5mu}}{\mathop{\bigcup\mkern-13mu\cdot\mkern5mu}} }\newcommand{\udot}{\cup\mkern-12.5mu\cdot\mkern6.25mu\!}\require{AMScd}\newcommand{\d}[1][]{\mathrm{d}^{#1} }
(Positive) Measure
\mu:\mathcal{R}\rightarrow\bar{\mathbb{R} }_{\ge0}
For a \sigma-ring, \mathcal{R}
Properties
\forall\overbrace{(A_n)_{n=1}^\infty }^{\begin{array}{c}\text{pairwise}\\\text{disjoint}\end{array} }\subseteq\mathcal{R}[\mu\left(\bigudot_{n=1}^\infty A_n\right)=\sum^\infty_{n=1}\mu(A_n)]

Definition

A (positive) measure, \mu is a set function from a \sigma-ring, \mathcal{R} , to the positive extended real values[Note 1], \bar{\mathbb{R} }_{\ge 0} [1][2][3]:

  • \mu:\mathcal{R}\rightarrow\bar{\mathbb{R} }_{\ge0}

Such that:

  • \forall(A_n)_{n=1}^\infty\subseteq\mathcal{R}\text{ pairwise disjoint }[\mu\left(\bigudot_{n=1}^\infty A_n\right)=\sum_{n=1}^\infty\mu(A_n)] (\mu is a countably additive set function)
    • Recall that "pairwise disjoint" means \forall i,j\in\mathbb{N}[i\ne j\implies A_i\cap A_j=\emptyset]

Entirely in words a (positive) measure, \mu is:

Remember that every \sigma-algebra is a \sigma-ring, so this definition can be applied directly (and should be in the reader's mind) to \sigma-algebras

Terminology

For a set

We may say a set A\in\mathcal{R} (for a \sigma-ring \mathcal{R} ) is:

Term Meaning Example
Finite[1] if \mu(A)<\infty
  • A is finite
  • A is of finite measure
\sigma-finite[1] if \exists(A_n)_{n=1}^\infty\subseteq\mathcal{R}\forall i\in\mathbb{N}[A\subseteq\bigcup_{n=1}^\infty A_n\wedge \mu(A_i)<\infty]
  • In words: if there exists a sequence of sets in \mathcal{R} such that A is in their union and each set has finite measure.
  • A is \sigma-finite
  • A is of \sigma-finite measure

Of a measure

We may say a measure, \mu is:

Term Meaning Example
Finite[1] If every set in the \sigma-ring the measure is defined on is of finite measure
  • Symbolically, if: \forall A\in\mathcal{R}[\mu(A)<\infty]
  • \mu is a finite measure
\sigma-finite[1] If every set in the \sigma-ring the measure is defined on is of \sigma-finite measure
  • Symbolically, if: \forall A\in\mathcal{R}\exists(A_n)_{n=1}^\infty\subseteq\mathcal{R}\forall i\in\mathbb{N}[A\subseteq\bigcup_{n=1}^\infty A_n\wedge \mu(A_i)<\infty]
  • \mu is a \sigma-finite measure
Complete if \forall A\in\mathcal{R}\forall B\in\mathcal{P}(A)[(\mu(A)=0)\implies(B\in\mathcal{R})]
  • In words: for every set of measure 0 in \mathcal{R} every subset of that set is also in \mathcal{R}
  • \mu is a complete measure

Of a measure on a \sigma-algebra

If \mu:\mathcal{A}\rightarrow\bar{\mathbb{R} }_{\ge0} for a \sigma-algebra \mathcal{A} [Note 2] then we can define:

Term Meaning Example
Totally finite[1] if the measure of X is finite
  • Symbolically, if \mu(X)<\infty
  • \mu is totally finite
Totally \sigma-finite[1] if X is of \sigma-finite measure
  • Symbolically, if: \exists(A_n)_{n=1}^\infty\subseteq\mathcal{R}\forall i\in\mathbb{N}[X=\bigcup_{n=1}^\infty A_n\wedge \mu(A_i)<\infty]
  • \mu is totally \sigma-finite

Immediate properties

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Trivial

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[Expand]

Claim: \mu(\emptyset)=0


Properties


TODO: Countable subadditivity and so forth


In common with a pre-measure

[Expand]

  • Finitely additive: if A\cap B=\emptyset then \mu_0(A\udot B)=\mu_0(A)+\mu_0(B)

[Expand]

  • Monotonic: [Note 3] if A\subseteq B then \mu_0(A)\le\mu_0(B)

[Expand]

  • If A\subseteq B and \mu_0(A)<\infty then \mu_0(B-A)=\mu_0(B)-\mu(A)

[Expand]

  • Strongly additive: \mu_0(A\cup B)=\mu_0(A)+\mu_0(B)-\mu_0(A\cap B)

[Expand]

  • Subadditive: \mu_0(A\cup B)\le\mu_0(A)+\mu_0(B)

Related theorems

Examples

Trivial measures

Here \mathcal{R} is a \sigma-ring[Note 4]

  1. \mu:\mathcal{R}\rightarrow\{0,+\infty\} by \mu(A)=\left\{\begin{array}{lr} 0 & \text{if }A=\emptyset \\ +\infty & \text{otherwise} \end{array}\right.
    • Note that if we'd chosen a finite and non-zero value instead of +\infty it would not be a measure[Note 5], as take any non-empty A,B\in\mathcal{R} with A\cap B=\emptyset, for a measure we would have:
      • \mu(A\cup B)=\mu(A)+\mu(B), which will yield v=2v\implies v=0 contradicting that \mu maps non-empty sets to finite non-zero values
  2. \mu:\mathcal{R}\rightarrow\{0\} by \mu:A\mapsto 0 is the trivial measure.
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That this is the trivial measure

See also

Notes

  1. Jump up Recall \bar{\mathbb{R} }_{\ge0} is \mathbb{R}_{\ge0}\cup\{+\infty\}
  2. Jump up Remember a sigma-algebra is just a sigma-ring containing the entire space.
  3. Jump up Sometimes stated as monotone (it is monotone in Measures, Integrals and Martingales in fact!)
  4. Jump up Remember every \sigma-algebra is a \sigma-ring, so \mathcal{R} could just as well be a \sigma-algebra
  5. Jump up Unless \mathcal{R} was a trivial \sigma-algebra consisting of the empty set and another set.

References

Note: Inline with the Measure theory terminology doctrine the references do not define a measure exactly as such, only an object that fits the place we have named measure. This sounds like a huge discrepancy but as is detailed on that page, it isn't.

  1. Jump up to: 1.0 1.1 1.2 1.3 1.4 1.5 1.6 Measure Theory - Paul R. Halmos
  2. Jump up Measures, Integrals and Martingales - René L. Schilling
  3. Jump up Measure Theory - Volume 1 - V. I. Bogachev