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Predictable Functions 📂Measure Theory

Predictable Functions

Definition1

Let’s call (X,E)(X, \mathcal{E}) a measurable space. Let’s define the set Sf(α)S_{f}(\alpha) as follows.

Sf(α):={xX  f(x)>α}=f1((α,)),αR S_{f}(\alpha):=\left\{ x\in X\ |\ f(x) >\alpha \right\} = f^{-1}\left( (\alpha, \infty) \right),\quad \forall \alpha \in \mathbb{R}

If for every real number αR\alpha \in \mathbb{R}, Sf(α)ES_{f}(\alpha) \in \mathcal{E} holds, then the function f:XRf : X \to \overline{\mathbb{R}} taking extended real values is called E\mathcal{E}-measurable or simply measurable.

Explanation

Especially, if X=RX=\mathbb{R}, it is called Lebesgue measurable. When determining whether a function is measurable or not, it’s useful to check if it conforms to the above definition, and there’s a useful theorem for that.

Theorem

For the function f:XRf : X \to \overline{\mathbb{R}}, the following four conditions are equivalent:

  • (a) For every αR\alpha \in \mathbb{R}, Aα=Sf(α)={xX:f(x)>α}A_{\alpha} = S_{f}(\alpha) =\left\{ x\in X : f(x) > \alpha \right\} \in E\mathcal{E}.
  • (b) For every αR\alpha \in \mathbb{R}, Bα={xX:f(x)α}B_{\alpha}=\left\{ x\in X : f(x) \le \alpha \right\} \in E\mathcal{E}.
  • (c) For every αR\alpha \in \mathbb{R}, Cα={xX:f(x)α}C_{\alpha}=\left\{ x\in X : f(x) \ge \alpha \right\} \in E\mathcal{E}.
  • (d) For every αR\alpha \in \mathbb{R}, Dα={xX:f(x)<α}D_{\alpha}=\left\{ x\in X : f(x) < \alpha \right\} \in E\mathcal{E}.

Proof

First, since AαA_{\alpha} and BαB_{\alpha} are complements of each other, according to the property of σ-algebra (D2), (a) and (b) are equivalent. Similarly, (c) and (d) are equivalent. Therefore, showing that (a) and (c) are equivalent completes the proof.

σ\sigma-Algebra

Let’s say the set XX is given. A collection EP(X)\mathcal{E} \subset \mathcal{P}(X) of subsets of XX that satisfies the below conditions is called a σ\sigma-algebra:

  • (D1) ,XE\varnothing, X \in \mathcal{E}
  • (D2) EE    EcEE \in \mathcal{E} \implies E^c \in \mathcal{E}
  • (D3) EkE (kN)    k=1EkEE_{k} \in \mathcal{E}\ (\forall k \in \mathbb{N}) \implies \bigcup_{k=1}^\infty E_{k} \in \mathcal{E}
  • (D4) EkE ( kN)    k=1EkEE_{k} \in \mathcal{E}\ (\forall\ k \in \mathbb{N}) \implies \bigcap_{k=1}^\infty E_{k} \in \mathcal{E}

(a)     \implies (c)

Assuming condition (a) holds, for every nNn\in \mathbb{N}, Aα1nEA_{\alpha-\frac{1}{n}}\in\mathcal{E} holds. And Cα=n=1Aα1nC_{\alpha}=\bigcap_{n=1}^\infty A_{\alpha-\frac{1}{n}}. Therefore, by definition (D3) of the σ\sigma-algebra, CαEC_{\alpha} \in \mathcal{E} holds.

(c)     \implies (a)

Assuming condition (c) holds, for every nNn\in \mathbb{N}, Cα+1nEC_{\alpha+\frac{1}{n}}\in\mathcal{E} holds. And Aα=n=1Cα+1nA_{\alpha}=\bigcup_{n=1}^\infty C_{\alpha+\frac{1}{n}}. Therefore, by definition (D3) of the σ\sigma-algebra, AαEA_{\alpha} \in \mathcal{E} holds.


  1. Robert G. Bartle, The Elements of Integration and Lebesgue Measure (1995), p8 ↩︎