为提高水文模型参数识别的可靠性,融合自回归模型与马尔可夫链-蒙特卡洛方法(auto regressive model based modified Markov Chain-Monte Carlo,AR-MCMC),利用自回归模型刻画残差序列的自相关性,修正MCMC方法中的残差协方差矩阵。通过...为提高水文模型参数识别的可靠性,融合自回归模型与马尔可夫链-蒙特卡洛方法(auto regressive model based modified Markov Chain-Monte Carlo,AR-MCMC),利用自回归模型刻画残差序列的自相关性,修正MCMC方法中的残差协方差矩阵。通过新疆提孜那甫河流域融雪径流模型(SRM)的案例分析发现:融雪径流模拟的残差序列具有显著的自相关性;修正残差协方差矩阵后,边缘似然值更大;综合考虑多项评价指标,AR-MCMC方法在识别期与验证期推求的预测区间均优于MCMC方法;对比2种方法在识别期与验证期的纳什系数,采用AR-MCMC方法依次为0.86、0.89,而采用MCMC方法依次为0.84、0.87,即AR-MCMC方法获取的模型拟合效果更好。分析结果表明,相对于传统的MCMC方法,AR-MCMC方法能够更好地对研究区融雪径流过程进行模拟预测。展开更多
Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict,combined with the law of ammunition consumption under different damage grades,a Bayesian inference metho...Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict,combined with the law of ammunition consumption under different damage grades,a Bayesian inference method for ammunition demand based on Gompertz distribution is proposed.The Bayesian inference model based on Gompertz distribution is constructed,and the system contribution degree is introduced to determine the weight of the multi-source information.In the case where the prior distribution is known and the distribution of the field data is unknown,the consistency test is performed on the prior information,and the consistency test problem is transformed into the goodness of the fit test problem.Then the Bayesian inference is solved by the Markov chain-Monte Carlo(MCMC)method,and the ammunition demand under different damage grades is gained.The example verifies the accuracy of this method and solves the problem of ammunition demand prediction in the case of insufficient samples.展开更多
文摘为提高水文模型参数识别的可靠性,融合自回归模型与马尔可夫链-蒙特卡洛方法(auto regressive model based modified Markov Chain-Monte Carlo,AR-MCMC),利用自回归模型刻画残差序列的自相关性,修正MCMC方法中的残差协方差矩阵。通过新疆提孜那甫河流域融雪径流模型(SRM)的案例分析发现:融雪径流模拟的残差序列具有显著的自相关性;修正残差协方差矩阵后,边缘似然值更大;综合考虑多项评价指标,AR-MCMC方法在识别期与验证期推求的预测区间均优于MCMC方法;对比2种方法在识别期与验证期的纳什系数,采用AR-MCMC方法依次为0.86、0.89,而采用MCMC方法依次为0.84、0.87,即AR-MCMC方法获取的模型拟合效果更好。分析结果表明,相对于传统的MCMC方法,AR-MCMC方法能够更好地对研究区融雪径流过程进行模拟预测。
基金the Army Scientific Research(KYSZJWJK1744,012016012600B11403).
文摘Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict,combined with the law of ammunition consumption under different damage grades,a Bayesian inference method for ammunition demand based on Gompertz distribution is proposed.The Bayesian inference model based on Gompertz distribution is constructed,and the system contribution degree is introduced to determine the weight of the multi-source information.In the case where the prior distribution is known and the distribution of the field data is unknown,the consistency test is performed on the prior information,and the consistency test problem is transformed into the goodness of the fit test problem.Then the Bayesian inference is solved by the Markov chain-Monte Carlo(MCMC)method,and the ammunition demand under different damage grades is gained.The example verifies the accuracy of this method and solves the problem of ammunition demand prediction in the case of insufficient samples.
