def _general_approximating_model(self, beta, T, Z, R, Q, h_approx):
        """ Creates simplest kind of approximating Gaussian model

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

        T, Z, R, Q : np.array
            State space matrices used in KFS algorithm

        h_approx : float
            Value to use for the H matrix

        Returns
        ----------

        H : np.array
            Approximating measurement variance matrix

        mu : np.array
            Approximating measurement constants
        """     

        H = np.ones(self.data_length)*h_approx
        mu = np.zeros(self.data_length)

        return H, mu
Creates simplest kind of approximating Gaussian model

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

        T, Z, R, Q : np.array
            State space matrices used in KFS algorithm

        h_approx : float
            Value to use for the H matrix

        Returns
        ----------

        H : np.array
            Approximating measurement variance matrix

        mu : np.array
            Approximating measurement constants