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Lebesgue differentiation theorem (Theorem)

Let $f$ be a locally integrable function on $\mathbb{R}^n$ with Lebesgue measure $m$ i.e. $f\in L^1_{loc}(\mathbb{R}^n)$ Lebesgue's differentiation theorem basically says that for almost every $x$ the averages $$\frac{1}{m(Q)}\int_Q |f(y)-f(x)|dy$$ converge to $0$ when $Q$ is a cube containing $x$ and $m(Q)\rightarrow 0$

Formally, this means that there is a set $N\subset \mathbb{R}^n$ with $\mu(N)=0$ such that for every $x\notin N$ and $\varepsilon>0$ there exists $\delta>0$ such that, for each cube $Q$ with $x\in Q$ and $m(Q)<\delta$ we have $$\frac{1}{m(Q)}\int_Q|f(y)-f(x)|dy<\varepsilon.$$

For $n=1$ this can be restated as an analogue of the fundamental theorem of calculus for Lebesgue integrals. Given a $x_0\in \mathbb{R}$ $$\frac{d}{dx}\int_{x_0}^x f(t)dt = f(x)$$ for almost every $x$




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Cross-references: Lebesgue integrals, fundamental theorem of calculus, cube, converge, averages, Lebesgue measure, locally integrable function
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This is version 6 of Lebesgue differentiation theorem, born on 2003-02-11, modified 2003-06-17.
Object id is 4026, canonical name is LebesgueDifferentiationTheorem.
Accessed 5579 times total.

Classification:
AMS MSC28A15 (Measure and integration :: Classical measure theory :: Abstract differentiation theory, differentiation of set functions)

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Applications of Lebesgue Differentiation Theorem by Yesid on 2008-10-20 17:55:26
Is there any application of the Lebesgue Differentiation Theorem for probability theory?
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