class_docstr: Base Class for multivariate elliptical distributions, normal and t
  
  contains common initialization, and some common methods
  subclass needs to implement at least rvs and logpdf methods
class_docstr_tok: ["Base","Class","for","multivariate","elliptical","distributions",",","normal","and","t","contains","common","initialization",",","and","some","common","methods","subclass","needs","to","implement","at","least","rvs","and","logpdf","methods"]
docstr: return marginal distribution for variables given by indices
  
  this should be correct for normal and t distribution
  
  Parameters
  ----------
  indices : array_like, int
      list of indices of variables in the marginal distribution
  
  Returns
  -------
  mvdist : instance
      new instance of the same multivariate distribution class that
      contains the marginal distribution of the variables given in
      indices
code: def marginal(self, indices):
      """return marginal distribution for variables given by indices
  
      this should be correct for normal and t distribution
  
      Parameters
      ----------
      indices : array_like, int
          list of indices of variables in the marginal distribution
  
      Returns
      -------
      mvdist : instance
          new instance of the same multivariate distribution class that
          contains the marginal distribution of the variables given in
          indices
  
      """
      indices = np.asarray(indices)
      mean_new = self.mean[indices]
      sigma_new = self.sigma[indices[:, None], indices]
      args = [getattr(self, ea) for ea in self.extra_args]
      return self.__class__(mean_new, sigma_new, *args)
  
imports: import numpy
  from scipy import special
  from statsmodels.sandbox.distributions.multivariate import mvstdtprob
  from .extras import mvnormcdf
label_desc: Use image node_id 16 for calling the MVElliptical obj's underlying member method code with example usage: obj.marginal(indices) and returns: self