Re: [Numpy-discussion] Addition of new distributions: Polya-gamma

Thanks for putting together this clean implementation! My concern is that Polya-Gamma is not popular enough to warrant inclusion in NumPy, which tries very hard to limit scope these days. For example, Polya-Gamma isn’t implemented in scioy.stats and doesn’t have a Wikipedia page, both of which are generally much more inclusive than NumPy. On Sun, Dec 27, 2020 at 3:29 AM Tom Swirly wrote:

I'm just a lurker, but I spent a minute or two to look at that commit, which looks to be high quality. While I personally have not used this distribution, people I know use it all the time (for ML).

A quibble:

#define NPY_PI 3.141592653589793238462643383279502884 /* pi */

and the following defines which appear in numpy/random/src/distributions/random_polyagamma.c are already defined in numpy/core/include/numpy/npy_math.h

Probably it would be better to include that file instead, if it isn't already included.

DISCLAIMER: I checked none of the math other than passing my eyes over it.

On Sun, Dec 27, 2020 at 12:05 PM Zolisa Bleki wrote:

...

Hi All,

I would like to know if Numpy accepts addition of new distributions since the implementation of the Generator interface. If so, what is the criteria for a particular distribution to be accepted? The reason why i'm asking is because I would like to propose adding the Polya-gamma distribution to numpy, for the following reasons:

1) Polya-gamma random variables are commonly used as auxiliary variables during data augmentation in Bayesian sampling algorithms, which have wide-spread usage in Statistics and recently, Machine learning. 2) Since this distribution is mostly useful for random sampling, it since appropriate to have it in numpy and not projects like scipy [1]. 3) The only python/C++ implementation of the sampler available is licensed under GPLv3 which I believe limits copying into packages that choose to use a different license [2]. 4) Numpy's random API makes adding the distribution painless.

I have done preliminary work on this by implementing the distribution sampler as decribed in [3]; see: https://github.com/numpy/numpy/compare/master...zoj613:polyagamma . There is a more efficient sampling algorithm described in a later paper [4], but I chose not to start with that one unless I know it is worth investing time in.

I would appreciate your thoughts on this proposal.

Regards, Zolisa

Refs: [1] https://github.com/scipy/scipy/issues/11009 [2] https://github.com/slinderman/pypolyagamma [3] https://arxiv.org/pdf/1205.0310v1.pdf [4] https://arxiv.org/pdf/1405.0506.pdf

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