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Lebesgue Measurable Functions 📂Measure Theory

Lebesgue Measurable Functions

Definition 1

A function f:ERf: E \in \overline{ \mathbb{R} } is said to be (Lebesgue) measurable if for every interval IRI \subset \overline{ \mathbb{R} }, f1(I)={xR  f(x)I}M f^{-1} (I) = \left\{ x \in \mathbb{R} \ | \ f(x) \in I \right\} \in \mathcal{M} holds.


  • R=R{,+}\overline{ \mathbb{R} } = \mathbb{R} \cup \left\{ - \infty , + \infty \right\} refers to the extended real number space, which includes positive and negative infinity, in the 11-dimensional Euclidean space.

Equivalent Conditions

The following propositions are equivalent to each other:

  • (1): ff is a Lebesgue measurable function.
  • (2): For all rRr \in \mathbb{R}, f1(,r]Mf^{-1} ( - \infty , r ] \in \mathcal{M} holds.
  • (3): For all rRr \in \mathbb{R}, f1(r,)Mf^{-1} (r, \infty ) \in \mathcal{M} holds.
  • (4): For all rRr \in \mathbb{R}, f1(,r)Mf^{-1} ( - \infty , r ) \in \mathcal{M} holds.
  • (5): For all rRr \in \mathbb{R}, f1[r,)Mf^{-1} [r, \infty ) \in \mathcal{M} holds.

Theorem

  • [1]: A necessary and sufficient condition for ff to be measurable is that for every open set OO, f1(O)Mf^{-1} ( O ) \in \mathcal{M} holds.
  • [2]: A necessary and sufficient condition for fEf |_{E} to be measurable, given DED \subset E and DMD \in \mathcal{M}, is that fDf |_{D} and fEDf |_{E \setminus D} are measurable.
  • [3]: Continuous functions are measurable.
  • [4]: Indicator functions are measurable.
  • [5]: Monotonic functions are measurable.

  • fXf |_{X} denotes a contraction mapping that restricts the domain to XX and satisfies f=fXf = f |_{X}.
  • An Indicator Function refers to a function that is 11 if it belongs to a certain set, and 00 otherwise. 1E(x)=χE(x)={1,xE0,xE\displaystyle \mathbb{1}_{E} (x) = \chi _{E} (x) = \begin{cases} 1 & , x \in E \\ 0 & , x \notin E \end{cases} Note that this definition omits the condition EME \in \mathcal{M}, so care should be taken.

Explanation

For easier manipulation, it’s convenient to use the original definition of pre-image, f1(,r)={xE  f(x)<r}f^{-1} (-\infty , r) = \left\{ x \in E \ | \ f(x) < r \right\}.

If all intervals IRI \subset \mathbb{R} satisfy f1(I)={xR  f(x)I}Bf^{-1} (I) = \left\{ x \in \mathbb{R} \ | \ f(x) \in I \right\} \in \mathcal{B} under the conditions for a Lebesgue measurable function, it is called Borel measurable and referred to as a Borel function.

Extended real numbers R:=[,]\overline{\mathbb{R}} : = [ - \infty, \infty] include infinity as a point along with the entire set of real numbers. Although infinity has been a daunting and difficult concept in analysis, it is now just another entity to conquer. Don’t be too afraid, and try to regain the flexible thinking of your high school days.

Considering a general measurable space, [1] can also become the definition of a measurable function.

Proof

[1]

Considering closed intervals, it is sufficient to only consider open intervals since adding two points at each end of a closed interval suffices.


()(\Rightarrow)

Defining open intervals Ak:=(ak,)A_{k} := (a_{k}, \infty), Bk:=(bk,)B_{k} := (b_{k}, \infty), since ff is a measurable function, f1(Ak),f1(Bk)Mf^{-1} (A_{k}), f^{-1} (B_{k}) \in \mathcal{M} any open set ORO \subset \overline{ \mathbb{R} } can be represented as O=k=1AkBk\displaystyle O = \bigcup_{k=1}^{\infty} A_{k} \cap B_{k}, hence, f1(O)=f1[k=1AkBk]=k=1[f1(Bk)f1(Bk)]\displaystyle f^{-1} ( O ) = f^{-1} \left[ \bigcup_{k=1}^{\infty} A_{k} \cap B_{k} \right] = \bigcup_{k=1}^{\infty} \left[ f^{-1} (B_{k}) \cap f^{-1} (B_{k}) \right] by the properties of the σ-field, it follows that f1(O)Mf^{-1} ( O ) \in \mathcal{M}.


()(\Leftarrow) Since f1(O)Mf^{-1} ( O ) \in \mathcal{M} holds for all open sets ORO \subset \overline{ \mathbb{R} }, it also holds for all open intervals (a,b)R(a,b) \subset \overline{ \mathbb{R} }.

By the definition of a measurable function, ff is a measurable function.


  1. Capinski. (1999). Measure, Integral and Probability: p57. ↩︎