code: def neg_loglik(self, beta):
               
  
          lmda, Y, ___, theta = self._model(beta)
          return -np.sum(logpdf(Y, self.latent_variables.z_list[-3].prior.transform(beta[-3]), 
              loc=theta, scale=np.exp(lmda/2.0), 
              skewness = self.latent_variables.z_list[-4].prior.transform(beta[-4])))
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