Bracketing of a function class ($[\ell ,u],\Vert u-\ell {\Vert }_{L^1(P)}$)

A bracket $[\ell ,u]$ contains every measurable function $h$ satisfying $\ell \le h\le u$ pointwise. For a probability measure $P$, its $L^1(P)$ width is $P(u-\ell )$, with integrable endpoints. Finite bracketing at every positive width is a sufficient complexity condition for a uniform law of large numbers. Pointwise inequalities, or inequalities outside a single common null set, allow empirical inequalities to hold simultaneously throughout the class.

A measurable function $M$ is an integrable envelope of a function class for a class $\mathcal{H}$ under a probability law $P$ if $|h(x)|\le M(x)$ for every $h\in \mathcal{H}$ and $PM<\infty$. Such an envelope permits the dominated convergence theorem to control shrinking function brackets.

Suppose an integrable measurable-function class can be covered by finitely many function brackets of every positive $L^1(P)$ width. For an independent sample with common law $P$, the empirical measure satisfies ${\sup }_h|P_{n}h-Ph|\to 0$ on a common probability-one event. For one finite $\epsilon$-cover, the supremum is bounded by $\epsilon$ plus the largest empirical error among its endpoints. The strong law of large numbers makes that finite maximum vanish. Taking a countable sequence of widths decreasing to zero proves the assertion. A measurable supremum can be obtained from a pointwise separable function class; otherwise the probability-one-event formulation expresses the same pathwise conclusion.

If $\Theta$ is compact, $q(\theta ,x)$ is continuous in $\theta$ and measurable in $x$, and $\mathbb{E}{\sup }_{\Theta }|q(\theta ,X)|<\infty$, then an independent and identically distributed sample satisfies ${\sup }_{\Theta }|P_n{q}_{\theta }-P{q}_{\theta }|\to 0$ almost surely. Uniform continuity for each $x$, the dominated convergence theorem, and finite parameter nets produce arbitrarily narrow function brackets.