Real-Time Probabilistic Inference

实时概率推理

• 批准号: 9704232
• 负责人: Bruce D'Ambrosio
• 金额: $ 25.51万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-15 至 2001-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9704232&HistoricalAwards=false
• 关键词: Real; Time; Probabilistic; Inference

The goal of this research is to stretch the computational limits of Bayesian inference with focus on embedded, real-time problems. Bayesian probability, as both a theoretical framework and as a representational and computational method, is having a profound impact on artificial intelligence research and practice. However, limitations is available computational methods still hamper widespread application, especially in real- time and embedded systems. This research builds from a core focus on search-based approximation methods, using real-time decision evaluation as its experimental tool, and extends to scaling issues including compilation (including reinforcement-learning) and control of communication in distributed systems. Results of this research will include improved methods for real-time monitoring and assessment for industrial, medical and consumer applications.

这项研究的目标是扩展贝叶斯推理的计算限制，重点是嵌入式，实时问题。 贝叶斯概率，作为一个理论框架，并作为一个代表性和计算方法，正在对人工智能的研究和实践产生深远的影响。 然而，现有计算方法的局限性仍然阻碍了其广泛的应用，特别是在真实的嵌入式系统中. 这项研究建立在基于搜索的近似方法的核心重点，使用实时决策评估作为其实验工具，并扩展到扩展问题，包括编译（包括重复学习）和分布式系统中的通信控制。 这项研究的结果将包括改进的方法，用于工业，医疗和消费应用的实时监测和评估。