Measure, Measure Space, and Probability Measure

definitionAnalysisProbability

Measure, Measure Space, and Probability Measure

definitionAnalysisProbabilitydef:measure-measure-space-2026a
· by Claude-Fable-5, Aaron ·
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Reason: Initial published version; Phase 0 of the probability program, approved by Aaron.

Let (X,F)(X,\mathcal{F}) be a \reftext{def:sigma-algebra-measurable-space-2026a}{measurable space}.

Write [0,][0,\infty] for the set [0,){}[0,\infty)\cup\{\infty\}, where \infty is a formal symbol with the conventions a+=+a=a+\infty=\infty+a=\infty for all a[0,]a\in[0,\infty], a<a<\infty for all real a0a\ge 0, and 0=0=00\cdot\infty=\infty\cdot 0=0. The \textbf{sum} of a \reftext{def:sequence-in-set-2026a}{sequence} (am)mN(a_m)_{m\in\mathbb{N}} in [0,][0,\infty] is defined as follows: if every ama_m is real and the partial sums are \reftext{def:upper-bound-supremum-c54-2026b}{bounded above}, then mam\sum_m a_m is their least upper bound (which is also their \reftext{def:limit-sequence-real-c54-2026a}{limit}, as the partial sums are nondecreasing); otherwise mam=\sum_m a_m=\infty.

A \textbf{measure} on (X,F)(X,\mathcal{F}) is a function μ:F[0,]\mu:\mathcal{F}\to[0,\infty] such that μ()=0\mu(\varnothing)=0 and, for every sequence (Am)mN(A_m)_{m\in\mathbb{N}} of pairwise disjoint members of F\mathcal{F},

μ(mNAm)=mNμ(Am)\mu\Bigl(\bigcup_{m\in\mathbb{N}}A_m\Bigr)=\sum_{m\in\mathbb{N}}\mu(A_m)

(\textbf{countable additivity}). The triple (X,F,μ)(X,\mathcal{F},\mu) is a \textbf{measure space}.

The measure μ\mu is \textbf{finite} if μ(X)<\mu(X)<\infty; it is \textbf{σ\sigma-finite} if there is a sequence (Xm)mN(X_m)_{m\in\mathbb{N}} in F\mathcal{F} with X=mXmX=\bigcup_m X_m and μ(Xm)<\mu(X_m)<\infty for every mm; and it is a \textbf{probability measure} if μ(X)=1\mu(X)=1.

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