Bayesian filtering for stochastic dynamical systems via Markov chain Monte Carlo

机译：马尔可夫链蒙特卡洛法对随机动力系统的贝叶斯滤波

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Stochastic dynamical systems have been increasingly used in natural sciences. Data assimilation, which can effectively combine observation data and theoretical models, improves the applicability of dynamical models. In this study, a statistical data assimilation method, Bayesian filtering, is presented. Its performance is examined with a dynamical model of aquatic ecosystem. It is found that the new method can give a satisfactory state estimate and be applied to general dynamical model in biological and environmental sciences.

机译：随机动力学系统已越来越多地用于自然科学中。数据同化可以有效地将观测数据和理论模型结合起来，提高了动力学模型的适用性。在这项研究中，提出了一种统计数据同化方法，贝叶斯滤波。用水生生态系统动力学模型检查其性能。发现该新方法可以给出令人满意的状态估计，并可以应用于生物学和环境科学中的一般动力学模型。

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《BMEI 2012;International Conference on Biomedical Engineering and Informatics》|2012年|1562-1565|共4页
会议地点 Chongqing(CN);Chongqing(CN)
作者
Gao Meng; Chang Xinghua; Wang Xinxiu;
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Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, 264003, China;

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正文语种 eng
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专利

1. Bayesian and Non-Bayesian Analysis of Gamma Stochastic Frontier Models by Markov Chain Monte Carlo Methods [J] . Hideo Kozumi, Xingyuan Zhang Computational statistics . 2005,第4期

机译：马尔可夫链蒙特卡洛方法对伽玛随机边界模型的贝叶斯和非贝叶斯分析
2. A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems [J] . Yuan Shen, Cedric Archambeau, Dan Cornford, Journal of Signal Processing Systems . 2010,第1期

机译：部分观测的随机动力系统中变分法和马尔可夫链蒙特卡罗方法的比较
3. A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems [J] . Yuan Shen, Cedric Archambeau, Dan Cornford, Journal of signal processing systems for signal, image, and video technology . 2010,第1期

机译：部分观测的随机动力系统中变分法和马尔可夫链蒙特卡罗方法的比较
4. Bayesian filtering for stochastic dynamical systems via Markov chain Monte Carlo [C] . Gao Meng, Chang Xinghua, Wang Xinxiu International Conference on Biomedical Engineering and Informatics . 2012

机译：Markov Chain Monte Carlo的随机动力系统贝叶斯滤波
5. Bayesian modeling of highly structured systems using Markov chain Monte Carlo [D] . Byers, Simon David. 1998

机译：使用马尔可夫链蒙特卡洛对高度结构化系统进行贝叶斯建模
6. Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo [O] . Andrew Golightly, Darren J. Wilkinson 2011

机译：基于粒子马尔可夫链蒙特卡洛的随机生化网络模型的贝叶斯参数推断
7. A comparison of nonlinear population Monte Carlo and particle Markov chain Monte Carlo algorithms for Bayesian inference in stochastic kinetic models [O] . Koblents, Eugenia, Míguez, Joaquín 2014

机译：非线性种群蒙特卡罗与粒子马尔可夫的比较随机动力学中贝叶斯推断的蒙特卡罗链算法楷模

Bayesian filtering for stochastic dynamical systems via Markov chain Monte Carlo

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