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>[!idea] A quick preview
>In the future, one will almost always prove $3$ versions of theorems: one for almost-sure, one for $L^1$, and one for $L^p$. UI connect AS and $L^1$.
> [!example] Integrable random variables vanish on vanishing sets
> Let $X$ be an [[constructing the Lebesgue Integral|integrable]] random variable. Set
> $
> I_X(\delta) = \sup\left\{\EE[\abs{X}\id_A]: A\in \FFF, \PP(A)\leq \delta\right\}
> $
> Then $I_X(\delta)\downarrow 0$ as $\delta\downarrow 0$.
> [!proof]- This is clear
> Otherwise, $\exists \eps >0$ and $A_n\in \FFF$ such that $\PP(A_n)\leq 2^{-n}$ and $\EE(\abs{X}\id_{A_n}) \geq \eps$ for all $n$; by Borel-Cantelli $\PP(A_n \ \text{i.o.}) = 0$. We now manually arrange for a $\lim\sup$ situation: note that for all $n$,
> $
> \eps\leq \EE\left\{\abs{X}\id\left(\bigcup_{m\geq n}A_m\right)\right\}
> $
> The RHS is a series of decreasing functions in $n$ which are bounded by the integrable function $\abs{X}$ and converge to $\abs{X}\id(A_n\ \text{i.o.})$... which integrates to $0$. Contradiction.
This property is a smoothness condition which we enjoy. Let's try to apply it to family of integrable random variables $\mathcal{X}$.
>[!definition] Uniform Integrability
> Write
> $I_\mathcal{X} = \sup\{ I_X : X\in \mathcal{X}\}$
>
> $\mathcal{X}$ is ==**uniformly integrable**== or UI if $\mathcal{X}$ is bounded in $L^1$ and $I_\mathcal{X}(\delta)\downarrow 0$ as $\delta\downarrow 0$.
>
> This is valid even if $\mathcal{X}$ is defined on different $\sigma$-algebras.
> [!example]- In general, $I_\mathcal{X}(\delta)$ will not converge to $0$ as $\delta \to 0$.
> Let $X_n = n$ identically; then $I_\mathcal{X}(\delta) = \infty$.
>[!idea] Interpreting the Definition
> Observe that $\mathcal{X}$ is [[Bounded in Lp|bounded in $L^1$]] iff $I_\mathcal{X}(1) < \infty$, so the condition is just making sure $I_\mathcal{X}(\delta) < \infty$ for all $\delta$.
>[!claim] Just check the hardest sets
>$\mathcal{X}$ is UI iff $
> \sup\left\{ \EE(\abs{X}\id_{\abs{X}\geq K} ): X\in \mathcal{X}\right\}\to 0
>$ as $K\to \infty$.
>[!proof] Boring, TODO
Note that if $\mathcal{X}$ is bounded in $L^p$ for $p \in (1,\infty)$, then $\mathcal{X}$ is UI; on the flip side, $n\id(0, \frac1n)$ is in $L^1$ yet not UI.
>[!example] Large UI set
> For any integrable random variable $Y$, the set $\mathcal{X} = \{X: X \text{ is a random variable}, \abs{X}\leq Y\}$ is UI.
>[!example] Another Large UI set
>See [[Conditional Expectations are UI]].
The bridge is the [[L1 Convergence Theorem]].