Lebesgue's decomposition theorem

In mathematics, more precisely in measure theory, Lebesgue's decomposition theorem is a theorem which states that given $\mu$ and $\nu$ two σ-finite signed measures on a measurable space $(\Omega ,\Sigma ),$ there exist two σ-finite signed measures $\nu _{0}$ and $\nu _{1}$ such that:

$\nu =\nu _{0}+\nu _{1}\,$
$\nu _{0}\ll \mu$ (that is, $\nu _{0}$ is absolutely continuous with respect to $\mu$ )
$\nu _{1}\perp \mu$ (that is, $\nu _{1}$ and $\mu$ are singular).

These two measures are uniquely determined.

Lebesgue's decomposition theorem can be refined in a number of ways.

First, the decomposition of the singular part can refined:

\,\nu =\nu _{\mathrm {cont} }+\nu _{\mathrm {sing} }+\nu _{\mathrm {pp} }

where

μ_cont is the absolutely continuous part
μ_sing is the singular continuous part
μ_pp is the pure point part (a discrete measure).

Second, absolutely continuous measures are classified by the Radon–Nikodym theorem, and discrete measures are easily understood. Hence (singular continuous measures aside), Lebesgue decomposition gives a very explicit description of measures. The Cantor measure (the probability measure on the real line whose cumulative distribution function is the Cantor function) is an example of a singular continuous measure.

Related concepts

Lévy–Itō decomposition

The analogous decomposition for a stochastic processes is the Lévy–Itō decomposition: given a Lévy process X, it can be decomposed as a sum $X=X^{(1)}+X^{(2)}+X^{(3)}$ where:

$X^{(1)}$ is a Brownian motion with drift, corresponding to the absolutely continuous part;
$X^{(2)}$ is a compound Poisson process, corresponding to the pure point part;
$X^{(3)}$ is a square integrable pure jump martingale that almost surely has a countable number of jumps on a finite interval, corresponding to the singular continuous part.

Lebesgue's decomposition theorem

Refinement

Related concepts

Lévy–Itō decomposition

See also