Real Analysis – Understanding the Lebesgue Decomposition Theorem

real-analysis

The usual statement of the Lebesgue Decomposition Theorem says that given two $\sigma$-finite measures $\mu$ and $\nu$ on a measure space, we can decompose $\nu = \nu_1 + \nu_2$, where $\nu_1$ is absolutely continuous with respect to $\mu$ and $\nu_2$ and $\mu$ are mutually singular.

Wikipedia (link text) says that there is a "refinement" of this result, where the singular part $\nu_2$ can be further decomposed into a discrete measure and a singular continuous measure.

I understand what a discrete measure is, but what exactly is the definition of a singular continuous measure? I was also wondering if anyone knew of a reference for this refined result, since I haven't been able to find it anywhere.

Best Answer

That decomposition is commonly encountered in probability theory. In a more general setting, suppose that $\rho$ is a $\sigma$-finite measure on $\mathcal{B}(\mathbb{R}^n)$. Then there is a unique decomposition $\rho = \rho_{ac} + \rho_d + \rho_{cs}$, such that: 1) $\rho_{ac}$ is absolutely continuous, that is, $\rho_{ac}$ is zero on sets of Lebesgue measure zero; 2) $\rho_d$ is discrete, that is, $\rho_d$ is zero on the complement of some countable set $C$; 3) $\rho_{cs}$ is continuous singular, that is, $\rho_{cs}$ is zero at every point $x \in \mathbb{R}^n$ (= continuous measure), and is zero on the complement of some set $B$ of Lebesgue measure zero (= singular measure).

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