Seventh International Workshop on Objective Bayesian Methodology; Philadelphia, PA

第七届客观贝叶斯方法论国际研讨会；

• 批准号: 0924257
• 负责人: Lawrence Brown
• 金额: --
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0924257&HistoricalAwards=false
• 关键词: Seventh; International; Workshop; Objective; Bayesian

The investigator will coordinate a five-day Objective Bayes workshop to be held at the Wharton School of the University of Pennsylvania. This workshop will bring together leading researchers from around the world who are active in the area of objective prior methodology. It may be viewed as a continuation of previous international meetings convened over the past 12 years devoted to Objective Bayes theory and methodology. The main objectives of the meeting are to facilitate the exchange of recent research developments within this scientific community, to provide opportunities for new researchers and underrepresented groups to be knowledgeable and active in this important area of research, and to establish new collaborations that will channel efforts into pending problems and open new directions for investigation. Objective Bayesian methodology is, for the most part, oriented towards the development of prior distributions that can be used automatically, i.e., that do not require subjective input other than the specific probabilistic model chosen to describe the data. Accompanying the development of forms particularly suitable for particular types of applications is the study of computational techniques for their implementation and evaluation of both the theoretical and concrete implications of their use. Many of the topics proposed for the workshop reflect an emphasis on objective Bayesian methodology for general classes of practical applications such as spatial-temporal models, hierarchical random-effects models, multiple comparisons and goodness -of-fit. In the direction of specific applications there will be sessions on Bayesian applications in astrophysics and on Bayesian applications in Business and Marketing research. In a more theoretical direction, there will be sessions on the foundations of objective Bayes analysis, including a special retrospective examination of Harold Jeffreys' seminal contributions to the area, and on the unification of Bayesian and frequentist statistical methods. The principal behind these so-called objective Bayes methods also has a long history dating at least back to work of Laplace almost 200 years ago. But two much more recent developments have brought emphasis and importance to the use of these methods. One is the rapidly increasing massiveness and complexity of modern statistical data. This has put extreme and often unmanageable strains on the theory and implementation of conventional non-Bayesian methods. (At the same time such complexity almost completely denies any reasonable possibility of formulating reasonable subjective prior opinions needed for ordinary Bayesian analyses.) The other parallel factor is the development within the past two decades of a suite of mathematical and computational strategies for successfully computing appropriately structured objective Bayesian procedures in highly complex situations. Within this time span an expanding community of scholars has been investigating these objective Bayes models and methods. The current conference proposal is to organize a workshop devoted to the investigation of objective Bayes methods.

调查人员将协调为期五天的客观贝叶斯研讨会，将在宾夕法尼亚大学沃顿商学院举行。该研讨会将汇集来自世界各地的领先研究人员，他们活跃于客观的先验方法。它可以看作是过去12年中召集的以前的国际会议的延续，致力于客观贝叶斯理论和方法论。会议的主要目的是促进该科学界的最新研究发展的交流，为新的研究人员和代表性不足的团体提供机会，使他们在这一重要的研究领域中知识渊博，并积极积极，并建立新的合作，以将努力引入待定问题并开放新的研究指导。客观的贝叶斯方法在大多数情况下是针对可以自动使用的先前分布的开发，即，除了选择描述数据的特定概率模型外，不需要主观输入。伴随着特别适合特定类型应用的形式的开发是计算技术的研究，以实施和评估其使用的理论和具体含义。为研讨会提出的许多主题反映了针对实际应用的一般类别的目标贝叶斯方法，例如时空模型，层次随机效应模型，多重比较和良好 - 拟合。在特定应用的方向上，将在天体物理学以及贝叶斯在商业和市场研究中的贝叶斯应用方面进行会议。在更理论的方向上，将在目标贝叶斯分析的基础上进行会议，包括对哈罗德·杰弗里斯（Harold Jeffreys）对该地区的开创性贡献的特殊回顾性检查，以及贝叶斯和频繁统计方法的统一。这些所谓的客观贝叶斯方法背后的主要历史至少可以追溯到近200年前的拉普拉斯（Laplace）工作。但是，最近有两个近期的发展为使用这些方法带来了重点和重要性。一个是现代统计数据的质量和复杂性迅速增加。这给传统的非bayesian方法的理论和实施带来了极端且常常难以控制的菌株。 （与此同时，这种复杂性几乎完全否认了普通贝叶斯分析所需的合理的主观先前意见的任何合理可能性。）另一个平行因素是在过去的二十年中发展的数学和计算策略，以成功地计算出高度复杂的情况下的数学和计算策略。在此期间，一个不断扩大的学者社区一直在研究这些客观的贝叶斯模型和方法。当前的会议建议是组织一个专门研究目标贝叶斯方法的研讨会。