docstr: Return the fitted mean structure.
  
  Parameters
  ----------
  params : array_like
      The parameter vector, may be the full parameter vector, or may
      be truncated to include only the mean parameters.
  exog : array_like
      The design matrix for the mean structure.  If omitted, use the
      model's design matrix.
  linear : bool
      If True, return the linear predictor without passing through the
      link function.
  
  Returns
  -------
  A 1-dimensional array of predicted values
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code: def predict(self, params, exog=None, linear=False):
      """
      Return the fitted mean structure.
  
      Parameters
      ----------
      params : array_like
          The parameter vector, may be the full parameter vector, or may
          be truncated to include only the mean parameters.
      exog : array_like
          The design matrix for the mean structure.  If omitted, use the
          model's design matrix.
      linear : bool
          If True, return the linear predictor without passing through the
          link function.
  
      Returns
      -------
      A 1-dimensional array of predicted values
      """
  
      if exog is None:
          exog = self.exog
  
      q = exog.shape[1]
      pr = np.dot(exog, params[0:q])
  
      if not linear:
          pr = self.family.link.inverse(pr)
  
      return pr
  
imports: import numpy
  from scipy.optimize import minimize
  from scipy import sparse
  import statsmodels.base.model
  from statsmodels.iolib import summary2
  from statsmodels.genmod import families
  import pandas
  import warnings
  import patsy
label_desc: Use image node_id 9 for calling the _BayesMixedGLM obj's underlying member method code with example usage: obj.predict(params, exog, linear) and returns: pr