code: def approximating_model_reg(self, beta, T, Z, R, Q, h_approx, data, X, state_no):
               
  
          H = np.ones(data.shape[0])*h_approx
          mu = np.zeros(data.shape[0])
  
          return H, mu
docs: Creates approximating Gaussian state space model for Skewt measurement density
          
          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
              The variance of the measurement density
          
          data: np.array
              The univariate time series data
  
          X: np.array
              The regressors
  
          state_no : int
              Number of states
      
          Returns
          ----------
          H : np.array
              Approximating measurement variance matrix
          
          mu : np.array
              Approximating measurement constants