class_docstr: Subclass of Bayesian GMMs (BGMM)
  that implements Variational Bayes estimation of the parameters
class_docstr_tok: ["Subclass","of","Bayesian","GMMs","(","BGMM",")","that","implements","Variational","Bayes","estimation","of","the","parameters"]
code: def __init__(
      self,
      k=1,
      dim=1,
      means=None,
      precisions=None,
      weights=None,
      shrinkage=None,
      dof=None,
  ):
      BGMM.__init__(
          self, k, dim, means, precisions, weights, shrinkage, dof
      )
      self.scale = self.precisions.copy()
  
imports: import math
  import numpy
  import numpy.random
  from scipy.linalg import cholesky, eigvalsh, inv
  from scipy.special import gammaln
  from .gmm import GMM
  from .utils import kmeans
label_desc: Use image node_id 1 to create a new VBGMM object from inherited base classes: BGMM with example: obj = VBGMM(k, dim, means, precisions, weights, shrinkage, dof)