class_docstr: Class for Multivariate Normal Distribution
  
  uses Cholesky decomposition of covariance matrix for the transformation
  of the data
class_docstr_tok: ["Class","for","Multivariate","Normal","Distribution","uses","Cholesky","decomposition","of","covariance","matrix","for","the","transformation","of","the","data"]
docstr: cumulative distribution function
  
  Parameters
  ----------
  x : array_like
      can be 1d or 2d, if 2d, then each row is taken as independent
      multivariate random vector
  kwds : dict
      contains options for the numerical calculation of the cdf
  
  Returns
  -------
  cdf : float or array
      probability density value of each random vector
code: def cdf(self, x, **kwds):
      """cumulative distribution function
  
      Parameters
      ----------
      x : array_like
          can be 1d or 2d, if 2d, then each row is taken as independent
          multivariate random vector
      kwds : dict
          contains options for the numerical calculation of the cdf
  
      Returns
      -------
      cdf : float or array
          probability density value of each random vector
  
      """
      # lower = -np.inf * np.ones_like(x)
      # return mvstdnormcdf(lower, self.standardize(x), self.corr, **kwds)
      return mvnormcdf(x, self.mean, self.cov, **kwds)
  
imports: import numpy
  from scipy import special
  from statsmodels.sandbox.distributions.multivariate import mvstdtprob
  from .extras import mvnormcdf
label_desc: Use image node_id 3 for calling the MVNormal obj's underlying member method code with example usage: obj.cdf(x) and returns: mvnormcdf