Creates the structure of the model (model matrices, etc) for
        a Normal family ARIMAX model.

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

        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 _normal_model(self, beta):
        """ Creates the structure of the model (model matrices, etc) for
        a Normal family ARIMAX model.

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

        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 = self.y[self.max_lag:]

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

        # Constant and AR terms
        if self.ar == 0:
            mu = np.transpose(self.ar_matrix)
        elif self.ar == 1:
            mu = np.transpose(self.ar_matrix)*z[:-self.family_z_no-self.ma-len(self.X_names)][0]
        else:
            mu = np.matmul(np.transpose(self.ar_matrix),z[:-self.family_z_no-self.ma-len(self.X_names)])

        # X terms
        mu = mu + np.matmul(self.X[self.integ+self.max_lag:],z[self.ma+self.ar:(self.ma+self.ar+len(self.X_names))])

        # MA terms
        if self.ma != 0:
            mu = arimax_recursion(z, mu, Y, self.max_lag, Y.shape[0], self.ar, self.ma)

        return mu, Y