class_docstr: Inverse Wishart Distribution
  ----
  This class contains methods relating to the inverse wishart distribution for time series.
class_docstr_tok: ["Inverse","Wishart","Distribution","--","--","This","class","contains","methods","relating","to","the","inverse","wishart","distribution","for","time","series","."]
docstr: Parameters
  ----------
  v : float
      Nu parameter for the Inverse Wishart distribution
  
  Psi : float
      Psi parameter for the Inverse Wishart distribution
  
  transform : str
      Whether to apply a transformation - e.g. 'exp' or 'logit'
docstr_tok: ["Parameters","--","--","--","--","--","v",":","float","Nu","parameter","for","the","Inverse","Wishart","distribution","Psi",":","float","Psi","parameter","for","the","Inverse","Wishart","distribution","transform",":","str","Whether","to","apply","a","transformation","-","e.g",".","'exp","'","or","'logit","'"]
code: def __init__(self, v, Psi, transform=None, **kwargs):
      """
      Parameters
      ----------
      v : float
          Nu parameter for the Inverse Wishart distribution
  
      Psi : float
          Psi parameter for the Inverse Wishart distribution
  
      transform : str
          Whether to apply a transformation - e.g. 'exp' or 'logit'
      """
      super(InverseWishart, self).__init__(transform)
  
      self.covariance_prior = True
      self.v = v
      self.Psi = Psi
  
file_path: pyflux/pyflux/families/inverse_wishart.py
imports: import numpy
  import scipy.stats
  import scipy.special
  from ..None import inference
  from .family import Family
  from scipy.stats import invwishart
label_desc: Use image node_id 1 to create a new InverseWishart object from inherited base classes: Family with example: obj = InverseWishart(v, Psi, transform)