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almost everywhere (Definition)

Let $(X, \borel, \mu)$ 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 \begin{equation} \int_{X} f d\mu(x) \le \int_{X} g d\mu(x) \end{equation}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
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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 25383 times total.

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

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