Nonlinear and non-Gaussian state-space modeling with Monte Carlo simulations
提出两种基于蒙特卡洛模拟的非线性非正态滤波器，比数值积分等传统方法更易编程和计算，并扩展到预测和平滑，通过蒙特卡洛实验评估其统计性能。
We propose two nonlinear and nonnormal filters based on Monte Carlo simulation techniques. In terms of programming and computational requirements both filters are more tractable than other nonlinear filters that use numerical integration, Monte Carlo integration with importance sampling or Gibbs sampling. The proposed filters are extended to prediction and smoothing algorithms. Monte Carlo experiments are carried out to assess the statistical merits of the proposed filters.