parameters: (self, mean=None, cov=1, allow_singular=False, seed=None,
                   maxpts=None, abseps=1e-5, releps=1e-5)
docstring: Create a frozen multivariate normal distribution.
  
          Parameters
          ----------
          mean : array_like, optional
              Mean of the distribution (default zero)
          cov : array_like, optional
              Covariance matrix of the distribution (default one)
          allow_singular : bool, optional
              If this flag is True then tolerate a singular
              covariance matrix (default False).
          seed : None or int or np.random.RandomState instance, optional
              This parameter defines the RandomState object to use for drawing
              random variates.
              If None (or np.random), the global np.random state is used.
              If integer, it is used to seed the local RandomState instance
              Default is None.
          maxpts: integer, optional
              The maximum number of points to use for integration of the
              cumulative distribution function (default `1000000*dim`)
          abseps: float, optional
              Absolute error tolerance for the cumulative distribution function
              (default 1e-5)
          releps: float, optional
              Relative error tolerance for the cumulative distribution function
              (default 1e-5)
  
          Examples
          --------
          When called with the default parameters, this will create a 1D random
          variable with mean 0 and covariance 1:
  
          >>> from scipy.stats import multivariate_normal
          >>> r = multivariate_normal()
          >>> r.mean
          array([ 0.])
          >>> r.cov
          array([[1.]])
docstring_summary: Create a frozen multivariate normal distribution.
docstring_tokens: Create a frozen multivariate normal distribution .
function: def __init__(self, mean=None, cov=1, allow_singular=False, seed=None,
                   maxpts=None, abseps=1e-5, releps=1e-5):
          """
          Create a frozen multivariate normal distribution.
  
          Parameters
          ----------
          mean : array_like, optional
              Mean of the distribution (default zero)
          cov : array_like, optional
              Covariance matrix of the distribution (default one)
          allow_singular : bool, optional
              If this flag is True then tolerate a singular
              covariance matrix (default False).
          seed : None or int or np.random.RandomState instance, optional
              This parameter defines the RandomState object to use for drawing
              random variates.
              If None (or np.random), the global np.random state is used.
              If integer, it is used to seed the local RandomState instance
              Default is None.
          maxpts: integer, optional
              The maximum number of points to use for integration of the
              cumulative distribution function (default `1000000*dim`)
          abseps: float, optional
              Absolute error tolerance for the cumulative distribution function
              (default 1e-5)
          releps: float, optional
              Relative error tolerance for the cumulative distribution function
              (default 1e-5)
  
          Examples
          --------
          When called with the default parameters, this will create a 1D random
          variable with mean 0 and covariance 1:
  
          >>> from scipy.stats import multivariate_normal
          >>> r = multivariate_normal()
          >>> r.mean
          array([ 0.])
          >>> r.cov
          array([[1.]])
  
          """
          self._dist = multivariate_normal_gen(seed)
          self.dim, self.mean, self.cov = self._dist._process_parameters(
                                                              None, mean, cov)
          self.cov_info = _PSD(self.cov, allow_singular=allow_singular)
          if not maxpts:
              maxpts = 1000000 * self.dim
          self.maxpts = maxpts
          self.abseps = abseps
          self.releps = releps
function_tokens: def __init__ ( self , mean = None , cov = 1 , allow_singular = False , seed = None , maxpts = None , abseps = 1e-5 , releps = 1e-5 ) : self . _dist = multivariate_normal_gen ( seed ) self . dim , self . mean , self . cov = self . _dist . _process_parameters ( None , mean , cov ) self . cov_info = _PSD ( self . cov , allow_singular = allow_singular ) if not maxpts : maxpts = 1000000 * self . dim self . maxpts = maxpts self . abseps = abseps self . releps = releps