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: logarithm of 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
  -------
  logpdf : float or array
      probability density value of each random vector
  
  
  this should be made to work with 2d x,
  with multivariate normal vector in each row and iid across rows
  does not work now because of dot in whiten
code: def logpdf(self, x):
      """logarithm of 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
      -------
      logpdf : float or array
          probability density value of each random vector
  
  
      this should be made to work with 2d x,
      with multivariate normal vector in each row and iid across rows
      does not work now because of dot in whiten
  
      """
      x = np.asarray(x)
      x_whitened = self.whiten(x - self.mean)
      SSR = np.sum(x_whitened**2, -1)
      llf = -SSR
      llf -= self.nvars * np.log(2.0 * np.pi)
      llf -= self.logdetcov
      llf *= 0.5
      return llf
  
imports: import numpy
  from scipy import special
  from statsmodels.sandbox.distributions.multivariate import mvstdtprob
  from .extras import mvnormcdf
label_desc: Use image node_id 5 for calling the MVNormal0 obj's underlying member method code with example usage: obj.logpdf(x) and returns: llf