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Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory.

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Random variable as an integral of an indicator function

The layer cake representation of a non-negative measurable function, $X$, is applied in the proof of proposition 2.1 here.
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Random variable as an integral of an indicator function

This answer says that if $X$ is a random variable and $X_+ = \mathrm{max}(0, X)$, then $X_+ = \int_0^\infty I_{\{X > x\}}\mathrm{d}x$. I'd like to know how to derive this starting with $A \in \mathcal …
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