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"]
docstr: return the likelihood of the model for the data x
  the values are weighted by the components weights
  
  Parameters
  ----------
  x: array of shape (nb_samples, self.dim)
     the data used in the estimation process
  
  Returns
  -------
  like: array of shape(nb_samples, self.k)
        component-wise likelihood
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code: def likelihood(self, x):
      """
      return the likelihood of the model for the data x
      the values are weighted by the components weights
  
      Parameters
      ----------
      x: array of shape (nb_samples, self.dim)
         the data used in the estimation process
  
      Returns
      -------
      like: array of shape(nb_samples, self.k)
            component-wise likelihood
      """
      x = self.check_x(x)
      return self._Estep(x)
  
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 8 for calling the VBGMM obj's underlying member method code with example usage: obj.likelihood(x) and returns: self