Creates the structure of the model (model matrices etc) for mini batch model

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

        mini_batch : int
            Mini batch size for the data sampling

        Returns
        ----------
        mu : np.ndarray
            Contains the predicted values (location) for the time series

        Y : np.ndarray
            Contains the length-adjusted time series (accounting for lags)

def _mb_model(self, beta, mini_batch):
        """ Creates the structure of the model (model matrices etc) for mini batch model

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

        mini_batch : int
            Mini batch size for the data sampling

        Returns
        ----------
        mu : np.ndarray
            Contains the predicted values (location) for the time series

        Y : np.ndarray
            Contains the length-adjusted time series (accounting for lags)
        """     

        Y = np.array(self.data[self.max_lag:])

        sample = np.random.choice(len(Y), mini_batch, replace=False)

        Y = Y[sample]
        X = self.X[:, sample]

        # Transform latent variables
        z = np.array([self.latent_variables.z_list[k].prior.transform(beta[k]) for k in range(beta.shape[0])])

        return neural_network_tanh_mb(Y, X, z, self.units, self.layers, self.ar+len(self.X_names)), Y