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1.发明申请公开(公告)号：US20120185424A1
公开(公告)日：2012-07-19
申请号：US12862657
申请日：2010-08-24
申请人： Akhileswar Ganesh Vaidyanathan , Eric N. Jean , Mani Thomas , David Louis Hample , Michael Thomas McGowan , Jijun Wang , Eli T. Faulkner , Jay Dee Askren , Albert Josef Boehmler , Durban A. Frazer
发明人： Akhileswar Ganesh Vaidyanathan , Eric N. Jean , Mani Thomas , David Louis Hample , Michael Thomas McGowan , Jijun Wang , Eli T. Faulkner , Jay Dee Askren , Albert Josef Boehmler , Durban A. Frazer
IPC分类号： G06N5/02
CPC分类号： G06N7/005
摘要： The present invention relates to a method for generating hypotheses automatically from graphical models built directly from data. The method of the present invention links three key scientific concepts to enable hypothesis generation from data driven hypothesis-models: including the use of information theory based measures to identify informative feature subsets within the data; the automatic generation of graphical models from the informative data subsets identified from step one; and the application of optimization methods to graphical models to enable hypothesis generation. The integration of these three concepts can enable scalable approaches to hypothesis generation from large, complex data environments. The use of graphical models as the model representation can allow prior knowledge to be effectively integrated into the modeling environment.
摘要翻译： 本发明涉及一种从直接从数据构建的图形模型自动生成假设的方法。 本发明的方法链接三个关键的科学概念，以便从数据驱动的假设模型中产生假设：包括使用基于信息理论的度量来识别数据内的信息特征子集; 从第一步识别的信息数据子集自动生成图形模型; 并将优化方法应用于图形模型以实现假设生成。 这三个概念的集成可以实现从大型复杂数据环境生成假设的可扩展方法。 使用图形模型作为模型表示可以将先验知识有效地整合到建模环境中。
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2.发明申请SCALABLE, COMPUTATIONALLY EFFICIENT AND RAPID SIMULATION SUITED TO DECISION SUPPORT, ANALYSIS AND PLANNING 审中-公开可靠，有计划地有效和快速的模拟用于决策支持，分析和规划公开(公告)号：US20090055150A1
公开(公告)日：2009-02-26
申请号：US12198036
申请日：2008-08-25
摘要： The present invention provides a means for performing scalable, computationally efficient and rapid simulations of complex or complex adaptive systems realized through the dynamic interaction of multiple modeling components to generate outputs suited to decision support, analysis and planning. In the context of disease modeling, these outputs can be used for analyzing the impact of disease and the potential value of the use of pharmaceutical and non-pharmaceutical interventions.
摘要翻译： 本发明提供了一种用于通过多个建模组件的动态交互实现的复杂或复杂自适应系统的可伸缩的，计算上有效和快速的模拟，以产生适合于决策支持，分析和规划的输出的手段。 在疾病建模的背景下，这些产出可用于分析疾病的影响以及使用药物和非药物干预措施的潜在价值。
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3.发明申请公开(公告)号：US20110231356A1
公开(公告)日：2011-09-22
申请号：US12829241
申请日：2010-07-01
申请人： Akhileswar Ganesh Vaidyanathan , Eric N. Jean , Mani Thomas , David Louis Hample , Michael Thomas McGowan , Jijun Wang , Eli T. Faulkner , Jay Dee Askren , Albert Josef Boehmler , Durban A. Frazer
发明人： Akhileswar Ganesh Vaidyanathan , Eric N. Jean , Mani Thomas , David Louis Hample , Michael Thomas McGowan , Jijun Wang , Eli T. Faulkner , Jay Dee Askren , Albert Josef Boehmler , Durban A. Frazer
IPC分类号： G06N5/02
CPC分类号： G06N7/005
摘要： The present invention relates to a method for generating hypotheses automatically from graphical models built directly from data. The method of the present invention links three key scientific concepts to enable hypothesis generation from data driven hypothesis-models: including the use of information theory based measures to identify informative feature subsets within the data; the automatic generation of graphical models from the informative data subsets identified from step one; and the application of optimization methods to graphical models to enable hypothesis generation. The integration of these three concepts can enable scalable approaches to hypothesis generation from large, complex data environments. The use of graphical models as the model representation can allow prior knowledge to be effectively integrated into the modeling environment.
摘要翻译： 本发明涉及一种从直接从数据构建的图形模型自动生成假设的方法。 本发明的方法链接三个关键的科学概念，以便从数据驱动的假设模型中产生假设：包括使用基于信息理论的度量来识别数据内的信息特征子集; 从第一步识别的信息数据子集自动生成图形模型; 并将优化方法应用于图形模型以实现假设生成。 这三个概念的集成可以实现从大型复杂数据环境生成假设的可扩展方法。 使用图形模型作为模型表示可以将先验知识有效地整合到建模环境中。
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公开(公告)号：US07720779B1
公开(公告)日：2010-05-18
申请号：US11626354
申请日：2007-01-23
摘要： A system for the representation, editing, evaluation, and inference of graphical models is disclosed which can be used to construct and evaluate a graphical model or graphical network and to calculate inference values. An efficient method of updating graphical models is demonstrated, and provides the basis for an improved system for manipulation and evaluation of probabilistic models. The graphical network editor is useful in the construction of graphical modes such as Bayesian Networks. The graphical network and network graphical user interface (GUI) are used in conjunction with each other to model a system wherein failure probabilities and the current state of components are taken into account to monitor the health and progress of a system for an engineer or engineering software to evaluate and monitor. The evaluation is useful in the monitoring of assets and other real systems having multiple, dependent, and independently operating components such as a pump, a manufacturing plant, a production line, an assembly line, where asset health and quality control is a concern. The asset components each influencing some overall outcome of a system or situation. Success or failure or probability of success, probability of failure and health of the system can be monitored and manipulated by altering the values of prior probability and posterior probability values. Failure correlation between components can be evaluated wherein failure rates of asset is unknown. Production and quality can be monitored and altered.
摘要翻译： 公开了用于图形模型的表示，编辑，评估和推断的系统，其可用于构建和评估图形模型或图形网络并计算推断值。 演示了一种更新图形模型的有效方法，为改进概率模型的操作和评估系统提供依据。 图形网络编辑器在构建诸如贝叶斯网络之类的图形模式方面是有用的。 图形网络和网络图形用户界面（GUI）彼此结合使用以对系统建模，其中考虑故障概率和组件的当前状态以监视工程师或工程软件的系统的健康和进度 评估和监测。 该评估对于具有多个，依赖和独立运行的组件的资产和其他实际系统的监测是有用的，例如泵，制造工厂，生产线，装配线，其中资产健康和质量控制是关注的。 资产组成部分影响系统或情况的一些总体结果。 可以通过改变先验概率和后验概率值的值来监测和操纵成功或失败或成功概率，故障概率和系统健康状况。 可以评估组件之间的故障相关性，其中资产的故障率是未知的。 生产和质量可以监控和改变。
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