class_docstr: A multivariate normal random variable.
  
  The `mean` keyword specifies the mean. The `cov` keyword specifies the
  covariance matrix.
  
  Methods
  -------
  ``pdf(x, mean=None, cov=1, allow_singular=False)``
      Probability density function.
  ``logpdf(x, mean=None, cov=1, allow_singular=False)``
      Log of the probability density function.
  ``cdf(x, mean=None, cov=1, allow_singular=False, maxpts=1000000*dim, abseps=1e-5, releps=1e-5)``
      Cumulative distribution function.
  ``logcdf(x, mean=None, cov=1, allow_singular=False, maxpts=1000000*dim, abseps=1e-5, releps=1e-5)``
      Log of the cumulative distribution function.
  ``rvs(mean=None, cov=1, size=1, random_state=None)``
      Draw random samples from a multivariate normal distribution.
  ``entropy()``
      Compute the differential entropy of the multivariate normal.
  
  Parameters
  ----------
  x : array_like
      Quantiles, with the last axis of `x` denoting the components.
  %(_mvn_doc_default_callparams)s
  %(_doc_random_state)s
  
  Alternatively, the object may be called (as a function) to fix the mean
  and covariance parameters, returning a "frozen" multivariate normal
  random variable:
  
  rv = multivariate_normal(mean=None, cov=1, allow_singular=False)
      - Frozen object with the same methods but holding the given
        mean and covariance fixed.
  
  Notes
  -----
  %(_mvn_doc_callparams_note)s
  
  The covariance matrix `cov` must be a (symmetric) positive
  semi-definite matrix. The determinant and inverse of `cov` are computed
  as the pseudo-determinant and pseudo-inverse, respectively, so
  that `cov` does not need to have full rank.
  
  The probability density function for `multivariate_normal` is
  
  .. math::
  
      f(x) = \frac{1}{\sqrt{(2 \pi)^k \det \Sigma}}
             \exp\left( -\frac{1}{2} (x - \mu)^T \Sigma^{-1} (x - \mu) \right),
  
  where :math:`\mu` is the mean, :math:`\Sigma` the covariance matrix,
  and :math:`k` is the dimension of the space where :math:`x` takes values.
  
  .. versionadded:: 0.14.0
  
  Examples
  --------
  >>> import matplotlib.pyplot as plt
  >>> from scipy.stats import multivariate_normal
  
  >>> x = np.linspace(0, 5, 10, endpoint=False)
  >>> y = multivariate_normal.pdf(x, mean=2.5, cov=0.5); y
  array([ 0.00108914,  0.01033349,  0.05946514,  0.20755375,  0.43939129,
          0.56418958,  0.43939129,  0.20755375,  0.05946514,  0.01033349])
  >>> fig1 = plt.figure()
  >>> ax = fig1.add_subplot(111)
  >>> ax.plot(x, y)
  
  The input quantiles can be any shape of array, as long as the last
  axis labels the components.  This allows us for instance to
  display the frozen pdf for a non-isotropic random variable in 2D as
  follows:
  
  >>> x, y = np.mgrid[-1:1:.01, -1:1:.01]
  >>> pos = np.dstack((x, y))
  >>> rv = multivariate_normal([0.5, -0.2], [[2.0, 0.3], [0.3, 0.5]])
  >>> fig2 = plt.figure()
  >>> ax2 = fig2.add_subplot(111)
  >>> ax2.contourf(x, y, rv.pdf(pos))
docstr: Multivariate normal probability density function.
  
  Parameters
  ----------
  x : array_like
      Quantiles, with the last axis of `x` denoting the components.
  %(_mvn_doc_default_callparams)s
  
  Returns
  -------
  pdf : ndarray or scalar
      Probability density function evaluated at `x`
  
  Notes
  -----
  %(_mvn_doc_callparams_note)s
docstr_tok: ["Multivariate","normal","probability","density","function",".","Parameters","--","--","--","--","--","x",":","array_like","Quantiles",",","with","the","last","axis","of","`","x","`","denoting","the","components",".","%","(","_mvn_doc_default_callparams",")","s","Returns","--","--","--","-","pdf",":","ndarray","or","scalar","Probability","density","function","evaluated","at","`","x","`","Notes","--","--","-","%","(","_mvn_doc_callparams_note",")","s"]
code: def pdf(self, x, mean=None, cov=1, allow_singular=False):
      """
      Multivariate normal probability density function.
  
      Parameters
      ----------
      x : array_like
          Quantiles, with the last axis of `x` denoting the components.
      %(_mvn_doc_default_callparams)s
  
      Returns
      -------
      pdf : ndarray or scalar
          Probability density function evaluated at `x`
  
      Notes
      -----
      %(_mvn_doc_callparams_note)s
  
      """
      dim, mean, cov = self._process_parameters(None, mean, cov)
      x = self._process_quantiles(x, dim)
      psd = _PSD(cov, allow_singular=allow_singular)
      out = np.exp(self._logpdf(x, mean, psd.U, psd.log_pdet, psd.rank))
      return _squeeze_output(out)
  
imports: import numpy
  import scipy.linalg
  from scipy._lib import doccer
  from scipy.special import gammaln
  from scipy._lib._util import check_random_state
  from scipy.stats import mvn
label_desc: Use image node_id 7 for calling the multivariate_normal_gen obj's underlying member method code with example usage: obj.pdf(x, mean, cov, allow_singular) and returns: _squeeze_output