code: def sde(self): 
           
          
          variance = float(self.variances.values) # this is initial variancve in Bayesian linear regression
          t0 = float(self.t0)
          
          F = np.array( ((0,1.0),(0,0) ))
          L = np.array( ((0,),(1.0,)) )
          Qc = np.zeros((1,1))
          H = np.array( ((1.0,0),) )
          
          Pinf   = np.zeros((2,2))
          P0 = np.array( ( (t0**2, t0), (t0, 1) ) ) * variance        
          dF = np.zeros((2,2,1))
          dQc    = np.zeros( (1,1,1) )
          
          dPinf = np.zeros((2,2,1))
          dP0 = np.zeros((2,2,1))
          dP0[:,:,0]  = P0 / variance
    
          return (F, L, Qc, H, Pinf, P0, dF, dQc, dPinf, dP0)
docs: Return the state space representation of the covariance.