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arXiv 1509.06352math.PR
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正倒向双重随机微分方程与扩散过程的最优滤波

Forward Backward Doubly Stochastic Differential Equations and the Optimal Filtering of Diffusion Processes

Feng Bao, Yanzhao Cao, Xiaoping Han

摘要:

在不使用Zakai方程的情况下,建立了正反向双重随机微分方程与最优滤波问题之间的联系。利用部分可观测马尔可夫扩散过程的条件律来表示正倒向双重随机微分方程的解。然后,它遵循的伴随的时间逆向前向后双重随机微分方程控制的最优滤波问题中的非归一化滤波密度的演变。

英文摘要:

The connection between forward backward doubly stochastic differential equations and the optimal filtering problem is established without using the Zakai's equation. The solutions of forward backward doubly stochastic differential equations are expressed in terms of conditional law of a partially observed Markov diffusion process. It then follows that the adjoint time-inverse forward backward doubly stochastic differential equations governs the evolution of the unnormalized filtering density in the optimal filtering problem.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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