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  4. 中科院海岸带环境过程与生态修复重点实验室
  5. 海岸带信息集成与综合管理实验室
Particle smoothing via Markov chain Monte Carlo in general state space models

文献类型：期刊论文

作者Gao, Meng1; Zhang, Hui2
出版日期2018
卷号9期号:2页码:181-188
关键词Dynamical systems Markov processes Numerical methods State estimation State space methods
ISSN号1752-5055
DOI10.1504/IJCSM.2018.091733
产权排序(1) Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai ; 264003, China ; (2) School of Natural and Applied Sciences, Northwestern Polytechnical University, Xi'an ; 710072, China
文献子类article
英文摘要Sequential Monte Carlo (SMC) methods (also known as particle filter) provide a way to solve the state estimation problem in nonlinear non-Gaussian state space models (SSM) through numerical approximation. Particle smoothing is one retrospective state estimation method based on particle filtering. In this paper, we propose a new particle smoother. The basic idea is easy and leads to a forward-backward procedure, where the Metropolis-Hastings algorithm is used to resample the filtering particles. The goodness of the new scheme is assessed using a nonlinear SSM. It is concluded that this new particle smoother is suitable for state estimation in complicated dynamical systems. Copyright © 2018 Inderscience Enterprises Ltd.
WOS研究方向Control Systems
语种英语
源URL[http://ir.yic.ac.cn/handle/133337/25215]  
专题烟台海岸带研究所_海岸带信息集成与综合管理实验室
烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
作者单位1.Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai; 264003, China;
2.School of Natural and Applied Sciences, Northwestern Polytechnical University, Xi'an; 710072, China
推荐引用方式
GB/T 7714		 Gao, Meng,Zhang, Hui. Particle smoothing via Markov chain Monte Carlo in general state space models[J],2018,9(2):181-188.
APA Gao, Meng,&Zhang, Hui.(2018).Particle smoothing via Markov chain Monte Carlo in general state space models.,9(2),181-188.
MLA Gao, Meng,et al."Particle smoothing via Markov chain Monte Carlo in general state space models".9.2(2018):181-188.

入库方式： OAI收割

来源：烟台海岸带研究所

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