logo

Lebesgue Measure 📂Measure Theory

Lebesgue Measure

Definition 1

Let us define the function m:M[0,]m : \mathcal{M} \to [0,\infty] with respect to EME \in \mathcal{M} as follows m(E):=m(E)m(E) := m^{ \ast } (E). mm is called the (Lebesgue) measure.


Description

The outer measure was neatly defined by m:P(R)[0,]m^{ \ast } : \mathscr{P}( \mathbb{R} ) \to [0, \infty], but it left something to be desired as a generalization of length. Limiting its domain to the real numbers’ sigma-field completed the ideal ‘generalization of length.’ This can be seen as a step back conditionally to meet the Carathéodory condition.

Of course, this is a special case in X=RX = \mathbb{R} when compared to general measures.

Basic Properties

Assuming A,B,EMA, B, E \in \mathcal{M} and for all nNn \in \mathbb{N}, let’s say An,Bn,MA_{n}, B_{n}, \in \mathcal{M}. The measure has the following properties:

  • [1]: AB    m(A)m(B)A \subset B \implies m(A) \le m(B)
  • [2]: If ABA \subset B then, m(A)<    m(BA)=m(B)m(A)m(A) < \infty \implies m(B \setminus A) = m(B) - m(A)
  • [3]: tR    m(E)=m(E+t)t \in \mathbb{R} \implies m(E) = m(E + t)
  • [4]: m(AB)=0    BMm(A)=m(B)m(A \triangle B) = 0 \implies B \in \mathcal{M} \\ m(A) = m(B)
  • [5]: For all ε>0,AR\varepsilon > 0, A \subset \mathbb{R}, there exists an open OO that satisfies the following. AOm(O)m(A)+ε A \subset O \\ m(O) \le m^{ \ast }(A) + \varepsilon
  • [6]: For all ARA \subset \mathbb{R}, there exists a sequence of open sets {On}\left\{ O_{n} \right\} that satisfies the following. AnOnm(nOn)=m(A) A \subset \bigcap_{n} O_{n} \\ m \left( \bigcap_{n} O_{n} \right) = m^{ \ast }(A)
  • [7]: AnAn+1    m(n=1An)=limnm(An)\displaystyle A_{n} \subset A_{n+1} \implies m \left( \bigcup_{n=1}^{\infty} A_{n} \right) = \lim_{n \to \infty} m (A_{n})
  • [8]: If An+1AnA_{n+1} \subset A_{n} then, m(A1)<    m(n=1An)=limnm(An)\displaystyle m(A_{1}) < \infty \implies m \left( \bigcap_{n=1}^{\infty} A_{n} \right) = \lim_{n \to \infty} m (A_{n})
  • [9]: m(i=1nAi)=i=1nm(Ai)\displaystyle m \left( \bigsqcup_{i=1}^{n} A_{i} \right) = \sum_{i = 1}^{n} m (A_{i})
  • [10]: mm is continuous in \emptyset.
  • [11]: Bn    m(Bn)0B_{n} \to \emptyset \implies m(B_{n}) \to 0

  • AB=(AB)(BA)A \triangle B = ( A \setminus B ) \cup ( B \setminus A ) is true.

Proof

[1]

Since m=mMm = m^{ \ast } |_{\mathcal{M}}, it naturally follows from the properties of outer measure.

[2]

First, it’s necessary to prove (BA)M(B \setminus A) \in \mathcal{M}. Given (BA)=B(RA)=BAc(B \setminus A) = B \cap (\mathbb{R} \setminus A) = B \cap A^{c} since AMA \in \mathcal{M}, thus AcMA^{c} \in \mathcal{M}. Therefore, (BA)M(B \setminus A) \in \mathcal{M} and (BA)A=(B \setminus A ) \cap A = \emptyset and (BA)A=B(B \setminus A ) \cup A = B, thus m(BA)+m(A)=m(B)m(B \setminus A ) + m(A) = m(B) is true. Assuming m(A)<m(A) < \infty, rearranging gives m(BA)=m(B)m(A)m(B \setminus A) = m(B) - m(A).

[3]

Since m=mMm = m^{ \ast } |_{\mathcal{M}}, it naturally follows from the properties of outer measure.

[4]

Since B=(AB)(BA)=A(AB)(BA)B = (A \cap B) \cup (B \setminus A) = A \setminus (A \setminus B) \cup (B \setminus A), BMB \in \mathcal{M} is true. Meanwhile, since m(AB)=0m(A \triangle B) = 0, m(AB)=0m(A \setminus B) = 0 and m(BA)=0m(B \setminus A) = 0. Therefore, m(B)=m(BA)+m(BA)=m(AB)+m(AB)=m(A) m(B) = m( B \setminus A) + m(B \cap A) = m( A \setminus B) + m(A \cap B) = m(A)

[7]

If we assume Bn:=AnAn1B_{n} :=A_{n} \setminus A_{n-1}, for iji \ne j, BiBj=B_{i} \cap B_{j} = \emptyset and n=1An=n=1Bn\displaystyle \bigcup_{n=1}^{\infty} A_{n} = \bigsqcup_{n=1}^{\infty} B_{n} are true. Therefore, m(n=1An)=m(n=1Bn)=n=1m(Bn)=limnk=1nm(Bk)=limnm(k=1nBk)=limnm(An) m \left( \bigcup_{n=1}^{\infty} A_{n} \right) = m \left( \bigsqcup_{n=1}^{\infty} B_{n} \right) = \sum_{n=1}^{\infty} m(B_{n}) = \lim_{n \to \infty} \sum_{k=1}^{n} m(B_{k}) = \lim_{n \to \infty} m \left( \bigsqcup_{k=1}^{n} B_{k} \right) = \lim_{n \to \infty} m \left( A_{n} \right)

[8]

Since (A1An)(A1An+1)(A_{1} \setminus A_{n} ) \subset (A_{1} \setminus A_{n+1} ), by [7] m(n=1(A1An))=limnm(A1An) m \left( \bigcup_{n=1}^{\infty} ( A_{1} \setminus A_{n} ) \right) = \lim_{ n \to \infty} m (A_{1} \setminus A_{n}) And since m(An)<m(A_{n}) < \infty, by [3] m(A1An)=m(A1)m(An) m (A_{1} \setminus A_{n}) = m(A_{1}) - m(A_{n}) Meanwhile, since n=1(A1An)=A1n=1An\displaystyle \bigcup_{n=1}^{\infty} (A_{1} \setminus A_{n}) = A_{1} \setminus \bigcap_{n=1}^{\infty} A_{n} m(n=1(A1An))=m(A1)m(n=1An) m \left( \bigcup_{n=1}^{\infty} (A_{1} \setminus A_{n}) \right) = m \left( A_{1} \right) - m \left( \bigcap_{n=1}^{\infty} A_{n} \right) Summarizing, m(n=1(A1An))=m(A1)limnm(An)=m(A1)m(n=1An) m \left( \bigcup_{n=1}^{\infty} (A_{1} \setminus A_{n}) \right) = m(A_{1}) - \lim_{n \to \infty} m(A_{n}) = m \left( A_{1} \right) - m \left( \bigcap_{n=1}^{\infty} A_{n} \right) Therefore, limnm(An)=m(n=1An)\displaystyle \lim_{n \to \infty} m(A_{n}) = m \left( \bigcap_{n=1}^{\infty} A_{n} \right)

Generalization


  1. Capinski. (1999). Measure, Integral and Probability: p35. ↩︎