Re: Basic spherical statistics in scipy

Last call to review the PR for the von Mises distribution which will
otherwise be merged in a week. PR link:
https://github.com/scipy/scipy/pull/17624

Am Sa., 4. Feb. 2023 um 12:03 Uhr schrieb Daniel Schmitz <
[email protected]>:

> Hi SciPy,
>
> I opened a PR for the Von-Mises Fisher distribution:
> https://github.com/scipy/scipy/pull/17624
>
> One thing needs more elaborate discussion: I added a fit method. So far,
> no other multivariate distribution has a fit method, so the API potentially
> sets a precedent.
>
> Currently it is implemented as fit(data) and returns the two distribution
> parameters `mu`, `kappa` . In principle, it is possible to also add the
> possibility for the user to fix one of the parameters, similar to what can
> be done with univariate distributions. If you have any thoughts on this,
> please join the discussion in the PR.
>
> Thanks!
>
> Am Sa., 19. Nov. 2022 um 17:17 Uhr schrieb Robert Kern <
> [email protected]>:
>
>> On Sat, Nov 19, 2022 at 3:50 AM Daniel Schmitz <
>> [email protected]> wrote:
>>
>>> Hi again everyone,
>>>
>>> the first milestones proposed here have been implemented:
>>> - sampling from the hypersphere
>>> - directional sample statistics (direction mean and mean resultant
>>> length)
>>>
>>> I would like to propose to further add the most commonly used analogue
>>> of the normal distribution on the hypersphere: the von Mises-Fisher
>>> distribution
>>> <https://en.wikipedia.org/wiki/Von_Mises%E2%80%93Fisher_distribution> (vMF).
>>> A reference implementations for sampling from it is available in
>>> geomstats
>>> <https://github.com/geomstats/geomstats/blob/f30c491a6da8cab38be48029d09eda2beec4defc/geomstats/geometry/hypersphere.py#L344> and
>>> fitting and evaluating pdf/logpdf should not be too difficult to implement
>>> by ourselves.
>>>
>>> Having worked with directional data a lot, I have seen many people
>>> struggle with these distributions. I do not think that all kinds of
>>> spherical distributions should become part of SciPy, but the vMF is so
>>> fundamental that it would be very valuable to the general community.
>>>
>>
>> I think that's reasonable.
>>
>> --
>> Robert Kern
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>

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