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Uniform Integrability 📂Measure Theory

Uniform Integrability

Definition

Let’s assume that a measure space (X,E,μ)( X , \mathcal{E} , \mu) is given.

Given a set of Lebesgue integrable functions ΦL1\Phi \subset \mathcal{L}^{1}, if for every ε>0\varepsilon>0, there exists δ>0\delta > 0 that satisfies μ(E)<δ    supfΦEfdμ<ε \mu (E) < \delta \implies \sup_{f \in \Phi} \int_{ E } \left| f \right| d \mu < \varepsilon then Φ\Phi is said to be uniformly integrable.

Explanation

The concept of uniform integrability approaches the set concept as suggested by the term Uniformly, meaning that if it belongs to Φ\Phi, there has to be a possibility for any function to make the norm l1l_{1} value simultaneously smaller than ε\varepsilon, that is, to have a narrow EE or in other words, a small δ>μ(E)\delta > \mu (E). Explaining it this way as a set might be mathematically rigorous but not intuitively easy to understand. As an example of a set of functions, think of the sequence {fn}n=1\left\{ f_{n} \right\}_{n=1}^{\infty}, which can be explained more easily as follows: μ(E)<δ    supnNEfndμ<ε \mu (E) < \delta \implies \sup_{n \in \mathbb{N}} \int_{ E } \left| f_{n} \right| d \mu < \varepsilon However, the reason for hesitation in using this representation is because a sequence is after all a countable set. From the standpoint of real analysis, which must serve as the basis of various theories, there seems to be an uncomfortable restriction of possibilities.

On the other hand, a good example of uniform integrability is the uniformly integrable martingale in the theory of stochastic processes.