We introduce a predictor-corrector discretisation scheme for the numerical integration of a class of stochastic differential equations and prove that it converges with weak order 1.0. The key feature of the new scheme is that it builds up sequentially (and recursively) in the dimension of the state space of the solution, hence making it suitable for approximations of high-dimensional state space models. We show, using the stochastic Lorenz 96 system as a test model, that the proposed method can operate with larger time steps than the standard Euler-Maruyama scheme and, therefore, generate valid approximations with a smaller computational cost. We also introduce the theoretical analysis of the error incurred by the new predictor-corrector scheme when used as a building block for discrete-time Bayesian filters for continuous-time systems. Finally, we assess the performance of several ensemble Kalman filters that incorporate the proposed sequential predictor-corrector Euler scheme and the standard Euler-Maruyama method. The numerical experiments show that the filters employing the new sequential scheme can operate with larger time steps, smaller Monte Carlo ensembles and noisier systems.
翻译:我们提出了一种预报-校正离散化方案,用于数值积分某类随机微分方程,并证明该方案具有弱收敛阶1.0.新方案的关键特点是按状态空间维数顺序(且递归地)构建,因此适用于高维状态空间模型的近似。我们使用随机Lorenz 96系统作为测试模型,在该模型上表明,与标准Euler-Maruyama方案相比,所提出的方法可以使用更大的时间步长,从而以更小的计算成本生成有效的近似值。我们还引入了新预测-校正Euler方案用于离散时间贝叶斯滤波器的构建块时产生的误差的理论分析。最后,我们评估了几个集合卡尔曼滤波器的性能,其中包括采用所提出的序贯预报-校正Euler方案和标准Euler-Maruyama方法的滤波器。数值实验表明,采用新的序贯方案的滤波器可以使用更大的时间步长、更小的蒙特卡洛集合和更嘈杂的系统。