Sample size determination typically relies on a power analysis based on a frequentist conditional approach. This latter can be seen as a particular case of the two-priors approach, which allows to build four distinct ...Sample size determination typically relies on a power analysis based on a frequentist conditional approach. This latter can be seen as a particular case of the two-priors approach, which allows to build four distinct power functions to select the optimal sample size. We revise this approach when the focus is on testing a single binomial proportion. We consider exact methods and introduce a conservative criterion to account for the typical non-monotonic behavior of the power functions, when dealing with discrete data. The main purpose of this paper is to present a Shiny App providing a user-friendly, interactive tool to apply these criteria. The app also provides specific tools to elicit the analysis and the design prior distributions, which are the core of the two-priors approach.展开更多
作战重心(Center of Gravity)是指战役体系中敌我双方的关键环节。作战重心评估是一个经验性、模糊性的过程。贝叶斯网络作为一种不确定知识表示模型,具有概率论及图论基础,对于解决复杂系统决策问题具有较强的优势,适合用于作战重心评...作战重心(Center of Gravity)是指战役体系中敌我双方的关键环节。作战重心评估是一个经验性、模糊性的过程。贝叶斯网络作为一种不确定知识表示模型,具有概率论及图论基础,对于解决复杂系统决策问题具有较强的优势,适合用于作战重心评估。文中提出并实现了一种基于贝叶斯网络推理的作战重心评估模型。通过该模型,可以定量地评估各个环节对于证据的重要程度,从而确定该作战过程中的作战重心。文中使用联合树(Clique Tree)算法进行贝叶斯网络精确推理,并详细阐述了推理过程中联合树建立,消息传递的过程。最后通过实例验证,基于贝叶斯网络推理的模型能够有效地对作战重心进行定量的评估。展开更多
The conventional Markov chain Monte Carlo （MCMC） method is limited to the selected shape and size of proposal distribution and is not easy to start when the initial proposal distribution is far away from the target ...The conventional Markov chain Monte Carlo （MCMC） method is limited to the selected shape and size of proposal distribution and is not easy to start when the initial proposal distribution is far away from the target distribution. To overcome these drawbacks of the conventional MCMC method, two useful improvements in MCMC method, adaptive Metropolis （AM） algorithm and delayed rejection （DR） algorithm, are attempted to be combined. The AM algorithm aims at adapting the proposal distribution by using the generated estimators, and the DR algorithm aims at enhancing the efficiency of the improved MCMC method. Based on the improved MCMC method, a Bayesian amplitude versus offset （AVO） inversion method on the basis of the exact Zoeppritz equation has been developed, with which the P- and S-wave velocities and the density can be obtained directly, and the uncertainty of AVO inversion results has been estimated as well. The study based on the logging data and the seismic data demonstrates the feasibility and robustness of the method and shows that all three parameters are well retrieved. So the exact Zoeppritz-based nonlinear inversion method by using the improved MCMC is not only suitable for reservoirs with strong-contrast interfaces and longoffset ranges but also it is more stable, accurate and antinoise.展开更多
文摘Sample size determination typically relies on a power analysis based on a frequentist conditional approach. This latter can be seen as a particular case of the two-priors approach, which allows to build four distinct power functions to select the optimal sample size. We revise this approach when the focus is on testing a single binomial proportion. We consider exact methods and introduce a conservative criterion to account for the typical non-monotonic behavior of the power functions, when dealing with discrete data. The main purpose of this paper is to present a Shiny App providing a user-friendly, interactive tool to apply these criteria. The app also provides specific tools to elicit the analysis and the design prior distributions, which are the core of the two-priors approach.
文摘作战重心(Center of Gravity)是指战役体系中敌我双方的关键环节。作战重心评估是一个经验性、模糊性的过程。贝叶斯网络作为一种不确定知识表示模型,具有概率论及图论基础,对于解决复杂系统决策问题具有较强的优势,适合用于作战重心评估。文中提出并实现了一种基于贝叶斯网络推理的作战重心评估模型。通过该模型,可以定量地评估各个环节对于证据的重要程度,从而确定该作战过程中的作战重心。文中使用联合树(Clique Tree)算法进行贝叶斯网络精确推理,并详细阐述了推理过程中联合树建立,消息传递的过程。最后通过实例验证,基于贝叶斯网络推理的模型能够有效地对作战重心进行定量的评估。
基金sponsorship of the National Natural Science Foundation of China (41674130, 41404088)the National Basic Research Program of China (973 Program, 2013CB228604, 2014CB239201)+1 种基金the National Oil and Gas Major Projects of China (2016ZX05027004-001, 2016ZX05002005-09HZ)the Fundamental Research Funds for the Central Universities (14CX02113A, 15CX08002A) for their funding in this research
文摘The conventional Markov chain Monte Carlo （MCMC） method is limited to the selected shape and size of proposal distribution and is not easy to start when the initial proposal distribution is far away from the target distribution. To overcome these drawbacks of the conventional MCMC method, two useful improvements in MCMC method, adaptive Metropolis （AM） algorithm and delayed rejection （DR） algorithm, are attempted to be combined. The AM algorithm aims at adapting the proposal distribution by using the generated estimators, and the DR algorithm aims at enhancing the efficiency of the improved MCMC method. Based on the improved MCMC method, a Bayesian amplitude versus offset （AVO） inversion method on the basis of the exact Zoeppritz equation has been developed, with which the P- and S-wave velocities and the density can be obtained directly, and the uncertainty of AVO inversion results has been estimated as well. The study based on the logging data and the seismic data demonstrates the feasibility and robustness of the method and shows that all three parameters are well retrieved. So the exact Zoeppritz-based nonlinear inversion method by using the improved MCMC is not only suitable for reservoirs with strong-contrast interfaces and longoffset ranges but also it is more stable, accurate and antinoise.
