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Lebesgue-Radon-Nikodym Theorem 📂Measure Theory

Lebesgue-Radon-Nikodym Theorem

Theorem1

A finite measure μ\mu, ν\nu on a measurable space (X,E)(X, \mathcal{E}) is given. Then, either μν\mu \perp \nu or there exist ϵ>0\epsilon>0, EEE \in \mathcal{E} satisfying the conditions below.

μ(E)>0andν(E)ϵμ(E) \mu (E) >0 \quad \text{and} \quad \nu (E) \ge \epsilon \mu (E)

Explanation

Although this theorem does not have a specific name, it is used as an auxiliary lemma when proving the Lebesgue-Radon-Nikodym theorem. It contains quite a powerful statement that the relationship between the two finite measures boils down to just one of two possibilities.

Proof

First, for natural numbers nn, the difference ν1nμ\nu-\dfrac{1}{n}\mu between the given two signed measures becomes a signed measure. Let’s refer to X=PnNnX=P_{n} \cup N_{n} as a decomposition of ν1nμ\nu-\dfrac{1}{n}\mu. And let’s define the sets PP, NN as below.

P:=n=1PnandN:=Pc=n=1Nn P:= \bigcup _{n=1} ^\infty P_{n} \quad \text{and} \quad N:=P^c=\bigcap _{n=1} ^\infty N_{n}

Then, since NN is a negative set with respect to ν1nμ\nu-\dfrac{1}{n}\mu, the following holds.

ν(N)1nμ(N)0    ν(N)1nμ(N) \nu (N)-\frac{1}{n}\mu (N) \le 0 \quad \implies \quad \nu (N) \le \frac{1}{n}\mu (N)

By assumption, μ(N)<\mu (N) < \infty holds, and since the above equation holds for all nn, we obtain the following.

ν(N)=0 \nu (N) =0

  • Case 1.

    Assuming μ(P)=0\mu (P)=0, then PN=XP\cup N =X, PN=P\cap N=\varnothing hold, and PP is ν\nu-null, NN is ν\nu-null, hence the following holds.

    νμ \nu \perp \mu

  • Case 2.

    Assuming μ(P)>0\mu (P)>0, then there exists at least one nn such that μ(Pn)>0\mu (P_{n})>0. And PnP_{n} is a positive set with respect to ν1nμ\nu -\dfrac{1}{n}\mu, hence the following holds.

    ν(Pn)1nμ(Pn)0    ν(Pn)1nμ(Pn) \nu (P_{n}) -\dfrac{1}{n}\mu ( P_{n}) \ge 0 \quad \implies \quad \nu (P_{n}) \ge \frac{1}{n}\mu (P_{n})

    1n\dfrac{1}{n} and PnP_{n} become ϵ\epsilon, EE that satisfy the theorem respectively.


  1. Gerald B. Folland, Real Analysis: Modern Techniques and Their Applications (2nd Edition, 1999), p89 ↩︎