code: def _general_approximating_model(self, beta, T, Z, R, Q, h_approx):
               
  
          H = np.ones(self.data_length)*h_approx
          mu = np.zeros(self.data_length)
  
          return H, mu
docs: 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