Math 2.0

Measure and integration Definition

Set-valued measure

Builds on Measurable space

Definition

Let (Ω,ℱ)(\Omega, \mathcal{F}) be a measurable space and let n≥1n \ge 1. A set-valued measure on (Ω,ℱ)(\Omega, \mathcal{F}) with values in ℝn\R^n is a map MM from ℱ\mathcal{F} to the nonempty subsets of ℝn\R^n such that:

  1. M(∅)={0}M(\emptyset) = \set{0}.
  2. For every sequence of pairwise disjoint sets A1,A2,…A_1, A_2, \dots in ℱ\mathcal{F} with union AA, M(A)={∑i=1∞xi:xi∈M(Ai) for all i, ∑i=1∞∣xi∣<∞}.M(A) = \set{\sum_{i=1}^{\infty} x_i : x_i \in M(A_i) \text{ for all } i, \ \sum_{i=1}^{\infty} \abs{x_i} < \infty}.

Remarks

Many authors also require the values to be closed, compact or convex, and some work in a Banach space instead of ℝn\R^n. Example: let μ\mu be a finite measure on ℱ\mathcal{F}, let K⊆ℝnK \subseteq \R^n be nonempty, compact and convex, and let M(A)=μ(A)KM(A) = \mu(A) K. Then M(∅)={0}M(\emptyset) = \set{0}. Given xi∈μ(Ai)Kx_i \in \mu(A_i) K, the series converges absolutely since KK is bounded, and its sum lies in μ(A)K\mu(A) K because KK is convex and closed. Conversely μ(A)k=∑iμ(Ai)k\mu(A) k = \sum_i \mu(A_i) k for k∈Kk \in K, so clause 2 holds. Non-example: let Ω={a,b}\Omega = \set{a, b} with counting measure μ\mu and K={0,1}K = \set{0, 1}, which is not convex. Then M(Ω)=2K={0,2}M(\Omega) = 2K = \set{0, 2}, while the sums from M({a})M(\set{a}) and M({b})M(\set{b}) form {0,1,2}\set{0, 1, 2}, so clause 2 fails. A vector measure ν\nu gives the set-valued measure A↦{ν(A)}A \mapsto \set{\nu(A)}.

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Further reading

  1. Web https://en.wikipedia.org/wiki/Set-valued_function
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DefinesSet-valued measure
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Changed2 November 2026

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