[Markov Chain Monte Carlo scheme for parameter uncertainty analysis in water quality model].
Parameter identification plays an important role in environmental model application. Markov Chain Monte Carlo method was introduced to estimate parameter uncertainty, since usual Bayes discrete methods were not applicable to produce posterior distribution of complicated environmental model due to the limit of computation. In order to study the performance and efficiency of MCMC, two case studies were used. Results indicate that, either sampling performance or sampling efficiency, MCMC method both has its special advantages in producing posterior distribution. Moreover, results of Gelman convergence diagnostics indicate that sampling sequence can converge to a stationary distribution. A key finding was that the MCMC scheme presented herein provided a powerful means of parameter identification and uncertainty analysis.
Wang
Jian-Ping
JP
Department of Environmental Science and Engineering, Tsinghua University, Beijing 100084, China. wangjp@tsinghua.org.cn
Cheng
Sheng-Tong
ST
Jia
Hai-Feng
HF
D008962:Models, Theoretical; D009010:Monte Carlo Method; D011786:Quality Control; D035501:Uncertainty; D014873:Water Pollutants / Q000032:analysis*; D014881:Water Supply / Q000032:analysis*
D014873:Water Pollutants