class_docstr: Class for Multivariate Normal Distribution
  
  original full version, kept for testing, new version inherits from
  MVElliptical
  
  uses Cholesky decomposition of covariance matrix for the transformation
  of the data
class_docstr_tok: ["Class","for","Multivariate","Normal","Distribution","original","full","version",",","kept","for","testing",",","new","version","inherits","from","MVElliptical","uses","Cholesky","decomposition","of","covariance","matrix","for","the","transformation","of","the","data"]
docstr: probability density function
  
  Parameters
  ----------
  x : array_like
      can be 1d or 2d, if 2d, then each row is taken as independent
      multivariate random vector
  
  Returns
  -------
  pdf : float or array
      probability density value of each random vector
code: def pdf(self, x):
      """probability density function
  
      Parameters
      ----------
      x : array_like
          can be 1d or 2d, if 2d, then each row is taken as independent
          multivariate random vector
  
      Returns
      -------
      pdf : float or array
          probability density value of each random vector
  
      """
  
      return np.exp(self.logpdf(x))
  
imports: import numpy
  from scipy import special
  from statsmodels.sandbox.distributions.multivariate import mvstdtprob
  from .extras import mvnormcdf
label_desc: Use image node_id 4 for calling the MVNormal0 obj's underlying member method code with example usage: obj.pdf(x) and returns: np