Initial commit for distribution package. Contains Exponential, Laplace, ...

...Normal, and Uniform
My method wasn't intended to be any different from what you already have done. You currently return the distribution parameters in the `newPriorValue` return variable in the FitPrior methods. Those same distribution parameters are what is currently stored in the distribution's state, so all I was suggesting is to not duplicate data (the distribution's state and the `newPriorValue` variable) and just use the distribution's state as `priorValue` if given a non-zero length priorWeight.

I like the Marshall and Unmarshall slice idea though as a separate convenience wrapper around `Parameter()` and puts it into a slice