What's the current best course of action when implementing python ops that require the inverse of the error function?
Currently the implementation of `distributions.Normal` uses an implementation of the normal CDF in python, which contains the numerical approximation of the error function within here.
I also see that there's something going on in the XLA world, with what appears to be a lower level implementation of the inverse error function.Would it be worthwhile refactoring the python implementation into an inverse error function, and an inverse normal cdf function which depends on it?
Hey,
Using special_math was my plan for erf, but I didn't see an inverse_erf in special_math, which was part of my reason for digging into the codebase (and finding the two places where it seemed to be). Could you link me to it if I've missed it (have tried searching the repo for "erfi", "erf_inverse" and "inverse_erf").
Thanks for clarifying on the XLA side of things, that squares with my understanding. I think my confusion stemmed from seeing functionality in XLA that I couldn't see exposed in the python front end.