docstr: Returns the evidence lower bound (ELBO) for the model.
docstr_tok: ["Returns","the","evidence","lower","bound","(","ELBO",")","for","the","model","."]
code: def vb_elbo(self, vb_mean, vb_sd):
      """
      Returns the evidence lower bound (ELBO) for the model.
      """
  
      fep_mean, vcp_mean, vc_mean = self._unpack(vb_mean)
      fep_sd, vcp_sd, vc_sd = self._unpack(vb_sd)
      tm, tv = self._lp_stats(fep_mean, fep_sd, vc_mean, vc_sd)
  
      def h(z):
          return -np.log(1 + np.exp(tm + np.sqrt(tv) * z))
  
      return self.vb_elbo_base(
          h, tm, fep_mean, vcp_mean, vc_mean, fep_sd, vcp_sd, vc_sd
      )
  
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 3 for calling the BinomialBayesMixedGLM obj's underlying member method code with example usage: obj.vb_elbo(vb_mean, vb_sd) and returns: self, unknown