class_docstr: This class implements Bayesian GMMs
  
  this class contains the following fields
  k: int,
     the number of components in the mixture
  dim: int,
       the dimension of the data
  means: array of shape (k, dim)
         all the means of the components
  precisions: array of shape (k, dim, dim)
              the precisions of the components
  weights: array of shape (k):
           weights of the mixture
  shrinkage: array of shape (k):
             scaling factor of the posterior precisions on the mean
  dof: array of shape (k)
       the degrees of freedom of the components
  prior_means: array of shape (k, dim):
               the prior on the components means
  prior_scale: array of shape (k, dim):
               the prior on the components precisions
  prior_dof: array of shape (k):
              the prior on the dof (should be at least equal to dim)
  prior_shrinkage: array of shape (k):
                   scaling factor of the prior precisions on the mean
  prior_weights: array of shape (k)
                 the prior on the components weights
  shrinkage: array of shape (k):
             scaling factor of the posterior precisions on the mean
  dof : array of shape (k): the posterior dofs
  
  fixme
  -----
  only 'full' precision is supported
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docstr: See bayes_factor(self, x, z, nperm=0, verbose=0)
docstr_tok: ["See","bayes_factor","(","self",",","x",",","z",",","nperm=0",",","verbose=0",")"]
code: def evidence(self, x, z, nperm=0, verbose=0):
      """
      See bayes_factor(self, x, z, nperm=0, verbose=0)
      """
      return self.bayes_factor(self, x, z, nperm, verbose)
  
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 16 for calling the BGMM obj's underlying member method code with example usage: obj.evidence(x, z, nperm, verbose) and returns: self