基于MCMC法的水质模型参数不确定性研究

参数识别是数学模型应用的前提.鉴于常用贝叶斯离散化方法在搜索复杂模型参数后验分布时的计算限制的原因,本文引入了MCMC采样法.为考察MCMC法对参数后验分布的搜索性能和效率,进行了2个案例研究.结果表明,MCMC法对参数后验分布的搜索,无论是搜索性能还是搜索效率,均表现出了独特的优越性.同时,Gelman收敛判别准则计算表明,MCMC采样序列均能稳定收敛到参数的后验分布上.可见,MCMC法适用于复杂环境模型的参数识别和不确定分析研究.

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.