2)}{\Gamma(\nu/2)\nu^{p/2}\pi^{p/2}|\Sigma|^{1/2}}
           \left[1 + \frac{1}{\nu} (\mathbf{x} - \boldsymbol{\mu})^{\top}
           \boldsymbol{\Sigma}^{-1}
           (\mathbf{x} - \boldsymbol{\mu}) \right]^{-(\nu + p)/2},

where :math:`p` is the dimension of :math:`\mathbf{x}`,
:math:`\boldsymbol{\mu}` is the :math:`p`-dimensional location,
:math:`\boldsymbol{\Sigma}` the :math:`p \times p`-dimensional shape
matrix, and :math:`\nu` is the degrees of freedom.

.. versionadded:: 1.6.0

References
----------
.. [1] Arellano-Valle et al. "Shannon Entropy and Mutual Information for
       Multivariate Skew-Elliptical Distributions". Scandinavian Journal
       of Statistics. Vol. 40, issue 1.

Examples
--------
The object may be called (as a function) to fix the `loc`, `shape`,
`df`, and `allow_singular` parameters, returning a "frozen"
multivariate_t random variable:

>>> import numpy as np
>>> from scipy.stats import multivariate_t
>>> rv = multivariate_t([1.0, -0.5], [[2.1, 0.3], [0.3, 1.5]], df=2)
>>> # Frozen object with the same methods but holding the given location,
>>> # scale, and degrees of freedom fixed.

Create a contour plot of the PDF.

>>> import matplotlib.pyplot as plt
>>> x, y = np.mgrid[-1:3:.01, -2:1.5:.01]
>>> pos = np.dstack((x, y))
>>> fig, ax = plt.subplots(1, 1)
>>> ax.set_aspect('equal')
>>> plt.contourf(x, y, rv.pdf(pos))

Nc