def sde(self): 
        
        variance = float(self.variance.values) # this is initial variancve in Bayesian linear regression
        
        F = np.array( ((0,1.0),(0,0) ))
        L = np.array( ((1.0,),(0,)) )
        Qc = np.array( ((variance,),) )
        H = np.array( ((1.0,0),) )
        
        Pinf   = np.array( ( (0, -0.5*variance ), (-0.5*variance, 0) ) )
        #P0 = Pinf.copy() 
        P0 = np.zeros((2,2))   
        #Pinf   = np.array( ( (t0, 1.0), (1.0, 1.0/t0) ) ) * variance
        dF = np.zeros((2,2,1))
        dQc    = np.ones( (1,1,1) )
        
        dPinf = np.zeros((2,2,1))
        dPinf[:,:,0] = np.array( ( (0, -0.5), (-0.5, 0) ) )
        #dP0 = dPinf.copy() 
        dP0 = np.zeros((2,2,1))
  
        return (F, L, Qc, H, Pinf, P0, dF, dQc, dPinf, dP0)
Dispatch to ImpalaClient.create_table. See that function's docstring
dataset/ETHPy150Open SheffieldML/GPy/GPy/kern/_src/sde_brownian.py/sde_Brownian.sde--crossover with--dataset/ETHPy150Open cloudera/ibis/ibis/impala/client.py/ImpalaDatabase.create_table