-1}(\kappa)}
           \exp\left(\kappa \mathbf{\mu}^T\mathbf{x}\right),

where :math:`\mathbf{\mu}` is the mean direction, :math:`\kappa` the
concentration parameter, :math:`d` the dimension and :math:`I` the
modified Bessel function of the first kind. As :math:`\mu` represents
a direction, it must be a unit vector or in other words, a point
on the hypersphere: :math:`\mathbf{\mu}\in S^{d-1}`. :math:`\kappa` is a
concentration parameter, which means that it must be positive
(:math:`\kappa>0`) and that the distribution becomes more narrow with
increasing :math:`\kappa`. In that sense, the reciprocal value
:math:`1/\kappa` resembles the variance parameter of the normal
distribution.

The von Mises-Fisher distribution often serves as an analogue of the
normal distribution on the sphere. Intuitively, for unit vectors, a
useful distance measure is given by the angle :math:`\alpha` between
them. This is exactly what the scalar product
:math:`\mathbf{\mu}^T\mathbf{x}=\cos(\alpha)` in the
von Mises-Fisher probability density function describes: the angle
between the mean direction :math:`\mathbf{\mu}` and the vector
:math:`\mathbf{x}`. The larger the angle between them, the smaller the
probability to observe :math:`\mathbf{x}` for this particular mean
direction :math:`\mathbf{\mu}`.

In dimensions 2 and 3, specialized algorithms are used for fast sampling
[2]_, [3]_. For dimensions of 4 or higher the rejection sampling algorithm
described in [4]_ is utilized. This implementation is partially based on
the geomstats package [5]_, [6]_.

.. versionadded:: 1.11

References
----------
.. [1] Von Mises-Fisher distribution, Wikipedia,
       https://en.wikipedia.org/wiki/Von_Mises%E2%80%93Fisher_distribution
.. [2] Mardia, K., and Jupp, P. Directional statistics. Wiley, 2000.
.. [3] J. Wenzel. Numerically stable sampling of the von Mises Fisher
       distribution on S2.
       https://www.mitsuba-renderer.org/~wenzel/files/vmf.pdf
.. [4] Wood, A. Simulation of the von mises fisher distribution.
       Communications in statistics-simulation and computation 23,
       1 (1994), 157-164. https://doi.org/10.1080/03610919408813161
.. [5] geomstats, Github. MIT License. Accessed: 06.01.2023.
       https://github.com/geomstats/geomstats
.. [6] Miolane, N. et al. Geomstats:  A Python Package for Riemannian
       Geometry in Machine Learning. Journal of Machine Learning Research
       21 (2020). http://jmlr.org/papers/v21/19-027.html

Examples
--------
**Visualization of the probability density**

Plot the probability density in three dimensions for increasing
concentration parameter. The density is calculated by the ``pdf``
method.

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.stats import vonmises_fisher
>>> from matplotlib.colors import Normalize
>>> n_grid = 100
>>> u = np.linspace(0, np.pi, n_grid)
>>> v = np.linspace(0, 2 * np.pi, n_grid)
>>> u_grid, v_grid = np.meshgrid(u, v)
>>> vertices = np.stack([np.cos(v_grid) * np.sin(u_grid),
...                      np.sin(v_grid) * np.sin(u_grid),
...                      np.cos(u_grid)],
...                     axis=2)
>>> x = np.outer(np.cos(v), np.sin(u))
>>> y = np.outer(np.sin(v), np.sin(u))
>>> z = np.outer(np.ones_like(u), np.cos(u))
>>> def plot_vmf_density(ax, x, y, z, vertices, mu, kappa):
...     vmf = vonmises_fisher(mu, kappa)
...     pdf_values = vmf.pdf(vertices)
...     pdfnorm = Normalize(vmin=pdf_values.min(), vmax=pdf_values.max())
...     ax.plot_surface(x, y, z, rstride=1, cstride=1,
...                     facecolors=plt.cm.viridis(pdfnorm(pdf_values)),
...                     linewidth=0)
...     ax.set_aspect('equal')
...     ax.view_init(azim=-130, elev=0)
...     ax.axis('off')
...     ax.set_title(rf"$\kappa={kappa}$")
>>> fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(9, 4),
...                          subplot_kw={"projection": "3d"})
>>> left, middle, right = axes
>>> mu = np.array([-np.sqrt(0.5), -np.sqrt(0.5), 0])
>>> plot_vmf_density(left, x, y, z, vertices, mu, 5)
>>> plot_vmf_density(middle, x, y, z, vertices, mu, 20)
>>> plot_vmf_density(right, x, y, z, vertices, mu, 100)
>>> plt.subplots_adjust(top=1, bottom=0.0, left=0.0, right=1.0, wspace=0.)
>>> plt.show()

As we increase the concentration parameter, the points are getting more
clustered together around the mean direction.

**Sampling**

Draw 5 samples from the distribution using the ``rvs`` method resulting
in a 5x3 array.

>>> rng = np.random.default_rng()
>>> mu = np.array([0, 0, 1])
>>> samples = vonmises_fisher(mu, 20).rvs(5, random_state=rng)
>>> samples
array([[ 0.3884594 , -0.32482588,  0.86231516],
       [ 0.00611366, -0.09878289,  0.99509023],
       [-0.04154772, -0.01637135,  0.99900239],
       [-0.14613735,  0.12553507,  0.98126695],
       [-0.04429884, -0.23474054,  0.97104814]])

These samples are unit vectors on the sphere :math:`S^2`. To verify,
let us calculate their euclidean norms:

>>> np.linalg.norm(samples, axis=1)
array([1., 1., 1., 1., 1.])

Plot 20 observations drawn from the von Mises-Fisher distribution for
increasing concentration parameter :math:`\kappa`. The red dot highlights
the mean direction :math:`\mu`.

>>> def plot_vmf_samples(ax, x, y, z, mu, kappa):
...     vmf = vonmises_fisher(mu, kappa)
...     samples = vmf.rvs(20)
...     ax.plot_surface(x, y, z, rstride=1, cstride=1, linewidth=0,
...                     alpha=0.2)
...     ax.scatter(samples[:, 0], samples[:, 1], samples[:, 2], c='k', s=5)
...     ax.scatter(mu[0], mu[1], mu[2], c='r', s=30)
...     ax.set_aspect('equal')
...     ax.view_init(azim=-130, elev=0)
...     ax.axis('off')
...     ax.set_title(rf"$\kappa={kappa}$")
>>> mu = np.array([-np.sqrt(0.5), -np.sqrt(0.5), 0])
>>> fig, axes = plt.subplots(nrows=1, ncols=3,
...                          subplot_kw={"projection": "3d"},
...                          figsize=(9, 4))
>>> left, middle, right = axes
>>> plot_vmf_samples(left, x, y, z, mu, 5)
>>> plot_vmf_samples(middle, x, y, z, mu, 20)
>>> plot_vmf_samples(right, x, y, z, mu, 100)
>>> plt.subplots_adjust(top=1, bottom=0.0, left=0.0,
...                     right=1.0, wspace=0.)
>>> plt.show()

The plots show that with increasing concentration :math:`\kappa` the
resulting samples are centered more closely around the mean direction.

**Fitting the distribution parameters**

The distribution can be fitted to data using the ``fit`` method returning
the estimated parameters. As a toy example let's fit the distribution to
samples drawn from a known von Mises-Fisher distribution.

>>> mu, kappa = np.array([0, 0, 1]), 20
>>> samples = vonmises_fisher(mu, kappa).rvs(1000, random_state=rng)
>>> mu_fit, kappa_fit = vonmises_fisher.fit(samples)
>>> mu_fit, kappa_fit
(array([0.01126519, 0.01044501, 0.99988199]), 19.306398751730995)

We see that the estimated parameters `mu_fit` and `kappa_fit` are
very close to the ground truth parameters.

c