# Measure Space

A Measure Space is a 2-Tuple composed of a Measurable Space (a set and its Power Set) and a Measure Function (μ).

## References

### 2009

• (Wikipedia, 2012) ⇒ http://en.wikipedia.org/wiki/Measure_(mathematics)
• QUOTE: 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 an Euclidean space, which assigns the conventional length, area, and volume of Euclidean geometry to suitable subsets of the n-dimensional Euclidean space Rn. 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 non-negative 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 sub-collection of all subsets; the so called measurable subsets, which are required to form a σ-algebra. This means that countable unions, countable intersections and complements of measurable subsets are measurable. Non-measurable 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. Indeed, their existence is a non-trivial 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.

• http://en.wikipedia.org/wiki/Measure_%28mathematics%29#Definition
• QUOTE: Let X be a set and Σ a σ-algebra over X. A function μ from Σ to the extended real number line is called a measure if it satisfies the following properties:
• Non-negativity: :$\displaystyle{ \mu(E)\geq 0 }$ for all $\displaystyle{ E\in\Sigma. }$
• Null empty set: :$\displaystyle{ \mu(\varnothing)=0. }$
• Countable additivity (or σ-additivity): For all countable collections $\displaystyle{ \{E_i\}_{i\in I} }$ of pairwise disjoint sets in Σ: :$\displaystyle{ \mu\Bigl(\bigcup_{i \in I} E_i\Bigr) = \sum_{i \in I} \mu(E_i). }$
• 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 $\displaystyle{ \mu(E)=\mu(E\cup\varnothing\cup\varnothing\cup\ldots)=\mu(E)+\sum_{i=0}^\infty \mu(\varnothing) }$ and $\displaystyle{ \sum_{i=0}^\infty \mu(\varnothing) }$ is finite if and only if the empty set has measure zero.

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 $\displaystyle{ (X, \Sigma_X) }$ is called a measurable space, the members of $\displaystyle{ \Sigma_X }$ are called measurable sets. If $\displaystyle{ (Y, \Sigma_Y) }$ is another measurable space then a function $\displaystyle{ f: X \to Y }$ is called measurable iff for every Y-measurable set $\displaystyle{ B \in \Sigma_Y }$, the inverse image is X-measurable i.e. $\displaystyle{ f^{-1}(B) \in \Sigma_X }$. The composition of measurable functions is measurable, making the measurable spaces and measurable functions a category, with the set of measurable functions as the arrows.

A triple (X, Σ, μ) is called a Template:Visible anchor. 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 authors. For more details see Radon measure.