Title :
Bayesian filtering for stochastic dynamical systems via Markov chain Monte Carlo
Author :
Meng Gao ; Xinghua Chang ; Xinxiu Wang
Author_Institution :
Yantai Inst. of Coastal Zone Res., Yantai, China
Abstract :
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.
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; biophysics; data assimilation; ecology; nonlinear dynamical systems; Bayesian filtering; Markov chain Monte Carlo; aquatic ecosystem dynamical model; biological sciences; environmental sciences; general dynamical model; statistical data assimilation method; stochastic dynamical systems;
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2012 5th International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4673-1183-0
DOI :
10.1109/BMEI.2012.6513095