code: def neg_loglik(self, beta):
               
  
          lmda, Y, _, theta = self._model(beta)
          return -np.sum(ss.t.logpdf(x=Y,
              df=self.latent_variables.z_list[-(len(self.X_names)*2)-2].prior.transform(beta[-(len(self.X_names)*2)-2]),
              loc=theta,scale=np.exp(lmda/2.0)))
docs: Creates the negative log-likelihood of the model
  
          Parameters
          ----------
          beta : np.array
              Contains untransformed starting values for latent variables
  
          Returns
          ----------
          The negative logliklihood of the model