code: def neg_loglik(self, beta):
               
  
          sigma2, Y, __ = self._model(beta)
          parm = np.array([self.latent_variables.z_list[k].prior.transform(beta[k]) for k in range(beta.shape[0])])
          return -np.sum(ss.norm.logpdf(Y, loc=parm[-1]*np.ones(sigma2.shape[0]), scale=np.sqrt(sigma2)))
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