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 < robert.kern@gmail.com>:
On Sat, Nov 19, 2022 at 3:50 AM Daniel Schmitz < danielschmitzsiegen@googlemail.com> 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/f30c491a6da8cab38be48029d09eda2b...> 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 _______________________________________________ SciPy-Dev mailing list -- scipy-dev@python.org To unsubscribe send an email to scipy-dev-leave@python.org https://mail.python.org/mailman3/lists/scipy-dev.python.org/ Member address: danielschmitzsiegen@googlemail.com