Futzing around thinking of distribution trees.
   class UniformValue:
     min: float
     max: float
   UniformValue(1, 2) + UniformValue(0, 4)
   Now we're back in probability again.
   If we use uniform distributions, then summing them will sum their
   range, but the output won't be uniform any more ... it'll be uh ...
   (websearches ..) an Irwin-Hall distribution? regardless it's a function
   that can be looked up.
   So say we have an Irwin-Hall distribution from 0 to 6 with specific PDF
   and CDF.  And suddenly we know that its value is 3.
   UniformValue(1, 2) + UniformValue(0, 4) == 3
   I don't know probability, but there must be some way to give new
   distributions here. Basically the two values completely depend on each
   other.  I guess it simplifies to:
   UniformValue(1,2) + Dependent_Value == 3 .
   Is this a ridiculous avenue? Or is this easily generalisable?