The inverse Kalman filter
提出逆卡尔曼滤波器，实现动态线性模型协方差矩阵与任意实值向量的精确矩阵向量乘法，计算成本线性；结合共轭梯度法加速矩阵求逆，应用于粒子相互作用函数的非参数估计。
Summary We introduce the inverse Kalman filter, which enables exact matrix-vector multiplication between a covariance matrix from a dynamic linear model and any real-valued vector with linear computational cost. We integrate the inverse Kalman filter with the conjugate gradient algorithm, which substantially accelerates the computation of matrix inversion for a general form of covariance matrix, where other approximation approaches may not be directly applicable. We demonstrate the scalability and efficiency of the proposed approach through applications in nonparametric estimation of particle interaction functions, using both simulations and cell trajectories from microscopy data.