Particle approximations of the score and observed information matrix in state space models with application to parameter estimation
状态空间模型中得分与观测信息矩阵的粒子近似及参数估计
Particle methods are popular computational tools for Bayesian inference in nonlinear non-Gaussian state space models. For this class of models, we present two particle algorithms to compute the score vector and observed information matrix recursively. The first algorithm is implemented with computational complexity and the second with complexity , where N is the number of particles. Although cheaper, the performance of the method degrades quickly, as it relies on the…