
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 a Euclidean space, which assigns the conventional length, area, and volume of Euclidean geometry to suitable subsets of the dimensional Euclidean space . For instance, the Lebesgue measure of the interval 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 nonnegative real number or +∞ to (certain) subsets of a set (''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 subcollection of all subsets; the socalled ''measurable'' subsets, which are required to form a . This means that countable unions, countable intersections and complements of measurable subsets are measurable. Nonmeasurable 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.〔Halmos, Paul (1950), Measure theory, Van Nostrand and Co.〕 Indeed, their existence is a nontrivial 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. ==Definition== Let be a set and a over . A function from to the extended real number line is called a measure if it satisfies the following properties: *Nonnegativity: For all in : . *Null empty set: . *Countable additivity (or ): For all countable collections $\backslash \_^\backslash infty\; E\_k\backslash right)=\backslash sum\_^\backslash infty\; \backslash mu(E\_k)$ One may require that at least one set has finite measure. Then the empty set automatically has measure zero because of countable additivity, because $\backslash mu(E)=\backslash mu(E\; \backslash cup\; \backslash varnothing)\; =\; \backslash mu(E)\; +\; \backslash mu(\backslash varnothing)$, so $\backslash mu(\backslash varnothing)\; =\; \backslash mu(E)\; \; \backslash mu(E)\; =\; 0$. If only the second and third conditions of the definition of measure above are met, and takes on at most one of the values , then is called a signed measure. The pair is called a measurable space, the members of are called measurable sets. If $\backslash left(X,\; \backslash Sigma\_X\backslash right)$ and $\backslash left(Y,\; \backslash Sigma\_Y\backslash right)$ are two measurable spaces, then a function $f\; :\; X\; \backslash to\; Y$ is called measurable if for every measurable set $B\; \backslash in\; \backslash Sigma\_Y$, the inverse image is measurable – i.e.: $f^(B)\; \backslash in\; \backslash Sigma\_X$. The composition of measurable functions is measurable, making the measurable spaces and measurable functions a category, with the measurable spaces as objects and the set of measurable functions as arrows. A triple is called a . A probability measure is a measure with total measure one – i.e. . 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 sources. For more details, see the article on Radon measures. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Measure (mathematics)」の詳細全文を読む スポンサード リンク
