Copula Particle Filters
提出一种Copula粒子滤波器算法，通过修改标准递归更新并应用Copula模型来避免重要性采样中的积分，同时获得似然函数用于参数推断，附有代码。
A novel analysis of the state space model is presented. It is shown that by modifying the standard recursive update it is possible to apply a copula model to eliminate a particular integral, which is typically performed using importance sampling. With Bayesian models, copulas have recently been shown to provide predictive densities directly, avoiding integrals altogether. As in every particle filter algorithm particles are generated; hence the proposed algorithm is named the Copula Particle Filter (CPF). As a by-product, the likelihood function of the model is obtained and used for parameter inference. Several illustrations and comparisons made with the standard updating schemes are provided. Supplementary material for this article, containing code, are available online.