Math 2.0

Measure and integration Definition

Finite measure

Builds on Measure spaceMeasureProbability measure

Definition

Let (X,Σ,μ)(X, \Sigma, \mu) be a measure space. The measure μ\mu is finite if μ(X)<∞\mu(X) < \infty.

Remarks

Lebesgue measure on the Borel sets of [0,1][0, 1] is finite, since μ([0,1])=1\mu([0, 1]) = 1. Lebesgue measure on the Borel sets of ℝ\R is not finite, since μ(ℝ)=∞\mu(\R) = \infty. If μ\mu is finite, then μ(A)≤μ(X)<∞\mu(A) \le \mu(X) < \infty for every A∈ΣA \in \Sigma, by monotonicity. Every probability measure is finite. If 0<μ(X)<∞0 < \mu(X) < \infty, then μ/μ(X)\mu / \mu(X) is a probability measure.

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To understand this page from the ground up, read these 4 pages in order. The foundations come first.

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  1. Measure concept
  2. Measure space concept
  3. Probability measure concept
  4. Finite measure definition

Further reading

  1. Web https://en.wikipedia.org/wiki/Finite_measure
About this page
Tag55RNFNN39R
URIm2://55RNFNN39R
Kinddefinition
WritingW0 stub
MSC28A12
DefinesFinite measure
UsesMeasure space, Measure, Probability measure
Changed2 November 2026

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