Mathematical Sciences: Computing Environments for Graphical Models

数学科学：图形模型的计算环境

• 批准号: 9211629
• 负责人: David Madigan
• 金额: $ 4.5万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-09-01 至 1996-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9211629&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Computing; Environments; Graphical

Graphical models can be defined as statistical models structured in terms of conditional independencies. These models are particularly successful in representing expert systems. This research will develop Bayesian methodology including elicitation of informative prior distributions, model updating and expression of model uncertainty for expert systems and some generalized applications. Expert systems, which are computer programs which mimic human experts, have found both commercial and scientific research application over the last decade. Their actual use in some areas has been limited because of the high level of uncertainty problem solvers must deal with in problematic areas. A rigorous probabilistic basis for expressing uncertainty can be incor- porated into these systems which will allow automated modifica- tion of the system in response to new information.

图形模型可以定义为统计模型 根据条件独立性来构建。 这些模型 在代表专家系统方面特别成功。 这 研究将开发贝叶斯方法，包括启发 信息先验分布，模型更新和表达 模型不确定性的专家系统和一些广义 应用. 专家系统是一种计算机程序， 人类专家发现，商业和科学研究 在过去的十年里，应用。 在某些领域的实际应用 由于高度的不确定性问题， 解决者必须处理有问题的区域。 严格的 表达不确定性的概率基础可以包括： 这些系统将允许自动修改， 系统响应新的信息。