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Definition of a monotone class

A monotone class is a family (i.e. class) M {\displaystyle M} of sets that is closed under countable monotone unions and also under countable monotone intersections. Explicitly, this means M {\displaystyle M} has the following properties:

1. if A 1 , A 2 , … ∈ M {\displaystyle A_{1},A_{2},\ldots \in M} and A 1 ⊆ A 2 ⊆ ⋯ {\displaystyle A_{1}\subseteq A_{2}\subseteq \cdots } then ⋃ i = 1 ∞ A i ∈ M , {\textstyle {\textstyle \bigcup \limits _{i=1}^{\infty }}A_{i}\in M,} and
2. if B 1 , B 2 , … ∈ M {\displaystyle B_{1},B_{2},\ldots \in M} and B 1 ⊇ B 2 ⊇ ⋯ {\displaystyle B_{1}\supseteq B_{2}\supseteq \cdots } then ⋂ i = 1 ∞ B i ∈ M . {\textstyle {\textstyle \bigcap \limits _{i=1}^{\infty }}B_{i}\in M.}

Monotone class theorem for sets

Monotone class theorem for sets—Let G {\displaystyle G} be an algebra of sets and define M ( G ) {\displaystyle M(G)} to be the smallest monotone class containing G . {\displaystyle G.} Then M ( G ) {\displaystyle M(G)} is precisely the 𝜎-algebra generated by G {\displaystyle G} ; that is σ ( G ) = M ( G ) . {\displaystyle \sigma (G)=M(G).}

Monotone class theorem for functions

Monotone class theorem for functions—Let A {\displaystyle {\mathcal {A}}} be a π-system that contains Ω {\displaystyle \Omega \,} and let H {\displaystyle {\mathcal {H}}} be a collection of functions from Ω {\displaystyle \Omega } to R {\displaystyle \mathbb {R} } with the following properties:

1. If A ∈ A {\displaystyle A\in {\mathcal {A}}} then 1 A ∈ H {\displaystyle \mathbf {1} _{A}\in {\mathcal {H}}} where 1 A {\displaystyle \mathbf {1} _{A}} denotes the indicator function of A . {\displaystyle A.}
2. If f , g ∈ H {\displaystyle f,g\in {\mathcal {H}}} and c ∈ R {\displaystyle c\in \mathbb {R} } then f + g {\displaystyle f+g} and c f ∈ H . {\displaystyle cf\in {\mathcal {H}}.}
3. If f n ∈ H {\displaystyle f_{n}\in {\mathcal {H}}} is a sequence of non-negative functions that increase to a bounded function f {\displaystyle f} then f ∈ H . {\displaystyle f\in {\mathcal {H}}.}

Then H {\displaystyle {\mathcal {H}}} contains all bounded functions that are measurable with respect to σ ( A ) , {\displaystyle \sigma ({\mathcal {A}}),} which is the 𝜎-algebra generated by A . {\displaystyle {\mathcal {A}}.}

Proof

The following argument originates in Rick Durrett's Probability: Theory and Examples.¹

The assumption Ω ∈ A , {\displaystyle \Omega \,\in {\mathcal {A}},} (2), and (3) imply that G = { A : 1 A ∈ H } {\displaystyle {\mathcal {G}}=\left\{A:\mathbf {1} _{A}\in {\mathcal {H}}\right\}} is a 𝜆-system. By (1) and the π−𝜆 theorem, σ ( A ) ⊆ G . {\displaystyle \sigma ({\mathcal {A}})\subseteq {\mathcal {G}}.} Statement (2) implies that H {\displaystyle {\mathcal {H}}} contains all simple functions, and then (3) implies that H {\displaystyle {\mathcal {H}}} contains all bounded functions measurable with respect to σ ( A ) . {\displaystyle \sigma ({\mathcal {A}}).}

Results and applications

As a corollary, if G {\displaystyle G} is a ring of sets, then the smallest monotone class containing it coincides with the 𝜎-ring of G . {\displaystyle G.}

By invoking this theorem, one can use monotone classes to help verify that a certain collection of subsets is a 𝜎-algebra.

The monotone class theorem for functions can be a powerful tool that allows statements about particularly simple classes of functions to be generalized to arbitrary bounded and measurable functions.

See also

• Dynkin system – Family closed under complements and countable disjoint unions
• π-𝜆 theorem – Family closed under complements and countable disjoint unionsPages displaying short descriptions of redirect targets
• π-system – Family of sets closed under intersection
• σ-algebra – Algebraic structure of set algebra

Citations

• Durrett, Richard (2019). Probability: Theory and Examples (PDF). Cambridge Series in Statistical and Probabilistic Mathematics. Vol. 49 (5th ed.). Cambridge New York, NY: Cambridge University Press. ISBN 978-1-108-47368-2. OCLC 1100115281. Retrieved November 5, 2020.

References

1. Durrett, Rick (2010). Probability: Theory and Examples (4th ed.). Cambridge University Press. p. 276. ISBN 978-0521765398. 978-0521765398 ↩