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Continuity of Probability

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Theorem
ProbabilityStochasticProcessesStochasticControl

Let (An)n∈N(A_{n})_{n\in\mathbb{N}} be an increasing events (An↗AA_{n}\nearrow A) or decreasing events (Anā†˜AA_{n}\searrow A). Then lim⁔nā†’āˆžP(An)=P(A)\lim_{n\to\infty}\mathbb{P}(A_n)=\mathbb{P}(A)

\begin{proof} Since AnāŠ†An+1A_{n}\subseteq A_{n+1} and A=ā‹ƒnAnA=\bigcup_{n}A_{n} then we define BnB_{n} such that: - B1=A1B_{1}=A_{1} - B2=A2āˆ–A1B_{2}=A_{2}\setminus A_{1} - B3=A3āˆ–A2B_{3}=A_{3}\setminus A_{2} - … Then A=ā‹ƒjBjA=\bigcup_{j} B_{j}and hence P(A)=P(ā‹ƒjBj)=āˆ‘jP(Bj)=lim⁔nā†’āˆžāˆ‘j=1nP(Bj)=lim⁔nā†’āˆžP(An)\mathbb{P}(A)=\mathbb{P}\left( \bigcup_{j}B_{j} \right)=\sum_{j}\mathbb{P}(B_{j})=\lim_{ n \to \infty } \sum_{j=1}^{n}\mathbb{P}(B_{j})=\lim_{ n \to \infty } \mathbb{P}(A_{n})same logic holds for decreasing events but with complements involved. \end{proof} ## Remark Simply a by-product of Measure.

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