code: def neg_loglik(self, beta):
               
  
          lmda, Y, ___ = self._model(beta)
          loc = np.ones(lmda.shape[0])*self.latent_variables.z_list[-2].prior.transform(beta[-2]) + self.latent_variables.z_list[-1].prior.transform(beta[-1])*np.exp(lmda/2.0)
          return -np.sum(ss.t.logpdf(x=Y,
              df=self.latent_variables.z_list[-3].prior.transform(beta[-3]),
              loc=loc,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