Mathematical Sciences: Conference on Multiple Decision Theory and Related Topics

数学科学：多重决策理论及相关主题会议

• 批准号: 9500062
• 负责人: James Berger
• 金额: $ 1万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-05-01 至 1996-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9500062&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Conference; Multiple; Decision

Re: DMS 95-00061, "Conference on Multiple Decision Theory and Related Topics" PI: James Berger Abstract: During June 8-12, 1995, a conference on Multiple Decision Theory and related topics will be held at Purdue University. The conference will consist of two parts, a traditional conference of invited and contributed talks in multiple decision theory, and an intensive workshop on the recent approach to multiple model selection called "intrinsic Bayes factors." The traditional part of the conference will consist of 27 invited talks by leaders in the field, including talks aimed at assessing the current status of research in the area and assessing future needs. The workshop phase will involve approximately 20 active researchers in development of intrinsic Bayes factors, a very recent and promising methodology which is applicable to virtually any model selection problem, nested or nonnested, without requiring asymptotics. The workshop phase will adopt a non-traditional framework that encourages discussion of unsolved problems, rather than solely emphasizing presentation of recent research. Multiple decision theory is concerned with situations in which there are two or more populations, and it is desired to select the "better" populations, rank order the populations, or make other decisions involving the comparison of populations. This type of scenario is common to a huge variety of applied problems in virtually all areas of statistical application. The area has seen a great deal of activity in recent years, and a conference on the subject involving the leaders in the field is very timely. The workshop phase of the conference will bring together a smaller group of researchers to study optimal ways to select among multiple statistical models. Model selection is typically the most important phase of a statistical analysis, and the new methodology that is to be discussed is extremely promising in terms of its ability to automatically select the best models. Level of Effort: The level of effort in the conference is being increased, by inclusion of the intensive workshop on intrinsic Bayes factors. The possibility of adding this workshop arose when it was realized that many of the active researchers on intrinsic Bayes factors would be at the Multiple Decision Theory conference; extending the conference for two days to incorporate this workshop thus seemed scientifically and financially wise. Budget Explanation: The request for $10,000 from NSF is not being changed. Approximately $5000 will, however, be used for the expenses of those attending the intensive workshop. (These individuals will, of course, also be participating in the earlier portion of the conference.) Additional expenses incurred by the increased level of effort will be met by Purdue University.

回复：DMS 95 - 00061，“多重决策理论及相关主题会议”PI：James Berger 摘要： 1995年6月8日至12日期间，将在普渡大学举行一次关于多重决策理论及相关主题的会议。会议将包括两个部分，一个传统的会议邀请和贡献的会谈在多重决策理论，和一个密集的研讨会上最近的方法，多模型选择称为"内在贝叶斯因素。"会议的传统部分将包括27个邀请该领域领导人的会谈，包括旨在评估该领域研究现状和评估未来需求的会谈。研讨会阶段将涉及大约20名积极的研究人员在发展的内在贝叶斯因素，一个非常新的和有前途的方法，适用于几乎任何模型选择问题，嵌套或非嵌套，而不需要渐近。研讨会阶段将采用一种非传统的框架，鼓励讨论尚未解决的问题，而不是仅仅强调介绍最近的研究。 多重决策理论关注的是有两个或多个总体的情况，并且希望选择“更好”的总体，对总体进行排序，或做出涉及总体比较的其他决策。这种情况在几乎所有统计应用领域的大量应用问题中都是常见的。近年来，该领域开展了大量活动，召开一次有该领域领导人参加的关于该主题的会议是非常及时的。会议的研讨会阶段将汇集一个较小的研究人员小组，研究在多个统计模型中进行选择的最佳方法。模型选择通常是统计分析中最重要的阶段，将要讨论的新方法在自动选择最佳模型的能力方面非常有前途。 努力程度： 通过列入关于内在贝叶斯因素的密集讲习班，会议的努力程度正在增加。当人们意识到许多内在贝叶斯因素的活跃研究人员将参加多重决策理论会议时，增加这个研讨会的可能性就出现了;因此，将会议延长两天以纳入这个研讨会似乎是科学和财政上明智的。 预算说明： 从NSF获得10，000美元的请求没有改变。然而，大约5000美元将用于参加密集讲习班的人员的费用。（当然，这些人也将参加会议的早期部分。普渡大学将承担因努力程度增加而产生的额外费用。