论文标题
向后SDE滤波器的内核学习方法
A Kernel Learning Method for Backward SDE Filter
论文作者
论文摘要
在本文中，我们开发了一种基于部分噪声观测值的随机动力学系统的状态，以估计随机动力学系统的状态。向前向后随机微分方程的系统用于传播目标动力学模型的状态，并应用贝叶斯推理以结合观测信息。为了表征整个状态空间中的动力学模型，我们引入了一种内核学习方法，以通过使用离散的近似密度值作为训练数据来学习目标状态条件概率密度函数的连续全局近似。数值实验表明，内核学习向后SDE非常有效且高效。
In this paper, we develop a kernel learning backward SDE filter method to estimate the state of a stochastic dynamical system based on its partial noisy observations. A system of forward backward stochastic differential equations is used to propagate the state of the target dynamical model, and Bayesian inference is applied to incorporate the observational information. To characterize the dynamical model in the entire state space, we introduce a kernel learning method to learn a continuous global approximation for the conditional probability density function of the target state by using discrete approximated density values as training data. Numerical experiments demonstrate that the kernel learning backward SDE is highly effective and highly efficient.
