关键词: 非线性状态空间模型, 混合效应模型, 序贯Monte Carlo方法, 辅助粒子滤波

Abstract:

Particle filter algorithm is a new algorithm for solving nonlinear system problems.Typically the particle filter uses importance resampling algorithm,which selects a priori distribution.But it is easily affected by external observation,leading to larger changes in weights.This paper introduces an auxiliary particle filter algorithm to improve this.The advantage of this algorithm is that the sample at the previous time is based on the current observational data,thus the sample obtained being closer to the true state.Simulation shows that the auxiliary particle filter algorithm is more effective than the sampling importance resampling.

Key words: nonlinear state space models;mixed effects models;sequential Monte Carlo methods;auxiliary particle filter