def mb_neg_loglik(self, beta, mini_batch):
        """ Creates the negative log-likelihood of the model

        Parameters
        ----------
        beta : np.array
            Contains untransformed starting values for latent variables

        mini_batch : int
            Size of each mini batch of data

        Returns
        ----------
        The negative logliklihood of the model
        """     

        lmda, Y, ___, theta = self._mb_model(beta, mini_batch)
        return -np.sum(logpdf(Y, self.latent_variables.z_list[-2].prior.transform(beta[-2]), 
            loc=theta, scale=np.exp(lmda/2.0), 
            skewness = self.latent_variables.z_list[-3].prior.transform(beta[-3])))
Creates the negative log-likelihood of the model

        Parameters
        ----------
        beta : np.array
            Contains untransformed starting values for latent variables

        mini_batch : int
            Size of each mini batch of data

        Returns
        ----------
        The negative logliklihood of the model