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The core idea is to create an object that acts like a `Quantity`, *but* instead of being a single value, is a set of samples from a *distribution*.  Any arbitrary operations applied to that object then act on the set of samples, and the result is essentially \"automatic\" uncertainty propogation (although at the cost of n_samples times the memory usage).\r\n\r\nSome items left to do (*possibly* doable by 3.0, but I don't want to jinx it by setting the milestone just yet...):\r\n- [ ]  Get all the tests passing (part of the point of creating this now is to ensure there aren't bigger problems on the various additional versions on CI)\r\n- [ ]  Documentation on this feature (including statistical caveats - esp when \"not enough samples\" is a problem)\r\n- [ ] Decide on the name: @adrn wants to call it `MarginalDistribution` instead of `Distribution` (I still favor `Distribution`, but @adrn  can weigh in).\r\n\r\nLonger-term additions (i.e. would be separate PRs)\r\n* A `JointDistribution` class that carries a set of distribution and allows e.g. easy corner plots, expectation values, etc (@adrn's idea)\r\n* Integrate this with @mhvk's work adapting standard uncertainty propagation following the normal approximation and automatic differentiation (#3715 I believe - that is *complementary* to this, not necessarily a replacement). 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