Comparisons of Maximum Likelihood Estimates and Bayesian Estimates for the Discretized Discovery Process Model石油科学：英文版论文
【题名】：Comparisons of Maximum Likelihood Estimates and Bayesian Estimates for the Discretized Discovery Process Model石油科学：英文版论文(Comparisons of Maximum Likelihood Estimates and Bayesian Estimates for the Discretized Discovery Process ModelShiYouKeXue：YingWenBanLunWen) 【关键词】：评价方法 贝叶斯定理 石油地质 北海 密度 马尔可夫链 【keywords】：PingJiaFangFa BeiYeSiDingLi ShiYouDiZhi BeiHai MiDu MaErKeFuLian 【作者】：GaoChunwen,XuJingzhen,RichardSinding-Larsen,      【来源】： 知识词典 【期刊名称】：石油科学：英文版(ShiYouKeXue：YingWenBan) 【国际标准刊号】：        【国内统一刊号】： 【作者单位】：[1]DepartmentofGeosciences,ZhejiangUniversity,Hangzhou,Zhejiang310027,China／／DaqingOilfieldExplorationLimitedCompany,Daqing,Heilongjiang163712,China [2]DepartmentofGeologyandMineralResourcesEngineering,NorwegianUniversityofScienceandTechnology,N-7491Trondheim,Norway([1]DepartmentofGeosciences,ZhejiangUniversity,Hangzhou,Zhejiang310027,China／／DaqingOilfieldExplorationLimitedCompany,Daqing,Heilongjiang163712,China [2]DepartmentofGeologyandMineralResourcesEngineering,NorwegianUniversityofScienceandTechnology,N-7491Trondheim,Norway) 【分类号】：P618.13      【页码】：-45-56      【出版年】：2005.2 【下载地址】：下载pdf全文         A Bayesian approach using Markov chain Monte Carlo algorithms has been developed to analyze Smith's discretized version of the discovery process model. It avoids the problems involved in the maximum likelihood method by effectively making use of the information from the prior distribution and that from the discovery sequence according to posterior probabilities. All statistical inferences about the parameters of the model and total resources can be quantified by drawing samples directly from the joint posterior distribution. In addition, statistical errors of the samples can be easily assessed and the convergence properties can be monitored during the sampling. Because the information contained in a discovery sequence is not enough to estimate all parameters, especially the number of fields, geologically justified prior information is crucial to the estimation. The Bayesian approach allows the analyst to specify his subjective estimates of the required parameters and his degree of uncertainty about the estimates in a clearly identified fashion throughout the analysis. As an example, this approach is applied to the same data of the North Sea on which Smith demonstrated his maximum likelihood method. For this case, the Bayesian approach has really improved the overly pessimistic results and downward bias of the maximum likelihood procedure.
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