Law of a random variable #
We introduce a predicate HasLaw X μ P stating that the random variable X has law μ under
the measure P. This is expressed as P.map X = μ. We also require X to be P-almost-everywhere
measurable. Indeed, if X is not almost-everywhere measurable then P.map X is defined to be 0,
so that HasLaw X 0 P would be true. The measurability hypothesis ensures nice interactions with
operations on the codomain of X.
See for instance HasLaw.comp, IndepFun.hasLaw_mul and IndepFun.hasLaw_add.
The predicate HasLaw X μ P registers the fact that the random variable X has law μ under
the measure P, in other words that P.map X = μ. We also require X to be AEMeasurable,
to allow for nice interactions with operations on the codomain of X. See for instance
HasLaw.comp, IndepFun.hasLaw_mul and IndepFun.hasLaw_add.
- aemeasurable : AEMeasurable X P