无条件期望一维倒向随机微分方程的一种新的数值解法

IF 0.3 Q4 STATISTICS & PROBABILITY

Random Operators and Stochastic Equations Pub Date : 2020-03-10 DOI:10.1515/rose-2020-2030

A. Sghir, Sokaina Hadiri

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引用次数: 3

摘要

摘要本文提出了一种新的不使用条件期望的一维倒向随机微分方程(BSDEs)的数值求解方法。解的近似形式是由末端条件产生的后向线性系统的解。我们的想法是从扩展卡尔曼滤波到非线性系统模型的启发，通过在确定性标称参考轨迹周围使用线性近似。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A new numerical method for 1-D backward stochastic differential equations without using conditional expectations

Abstract In this paper, we propose a new numerical method for 1-D backward stochastic differential equations (BSDEs for short) without using conditional expectations. The approximations of the solutions are obtained as solutions of a backward linear system generated by the terminal conditions. Our idea is inspired from the extended Kalman filter to non-linear system models by using a linear approximation around deterministic nominal reference trajectories.

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来源期刊

Random Operators and Stochastic Equations STATISTICS & PROBABILITY-

CiteScore

0.60

自引率

25.00%

发文量