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almost everywhere (Definition)

Let $ (X, \mathfrak{B}, \mu)$ be a measure space. A condition holds almost everywhere on $ X$ if it holds “with probability $ 1$,” i.e. if it holds everywhere except for a subset of $ X$ with measure 0. For example, let $ f$ and $ g$ be nonnegative functions on $ X$. Suppose we want a sufficient condition on functions $ f(x)$ and $ g(x)$ such that the relation

$\displaystyle \int_{X} f d\mu(x) \le \int_{X} g d\mu(x)$ (1)

holds. Certainly $ f(x)\leq g(x)$ for all $ x\in X$ is a sufficient condition, but in fact it's enough to have $ f(x)\leq g(x)$ almost surely on $ X$. In fact, we can loosen the above non-negativity condition to only require that $ f$ and $ g$ are almost surely nonnegative as well.

If $ X = [0,1]$, then $ g$ might be less than $ f$ on the Cantor set, an uncountable set with measure 0, and still satisfy the condition. We say that $ f \le g$ almost everywhere (often abbreviated a.e.).

Note that this term is the equivalent of the term “almost surely” from probabilistic measure theory.



"almost everywhere" is owned by mathcam. [ full author list (2) | owner history (1) ]
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Other names:  almost surely, a.s., a.e., almost all

Attachments:
continuous almost everywhere versus equal to a continuous function almost everywhere (Example) by Wkbj79
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Cross-references: uncountable set, Cantor set, relation, sufficient, functions, measure, subset, measure space
There are 78 references to this entry.

This is version 4 of almost everywhere, born on 2002-02-16, modified 2004-04-09.
Object id is 2002, canonical name is AlmostSurely.
Accessed 20892 times total.

Classification:
AMS MSC60A10 (Probability theory and stochastic processes :: Foundations of probability theory :: Probabilistic measure theory)

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