Algorithmic Methods in Probability Models

概率模型中的算法方法

• 批准号: 8601203
• 负责人: Marcel Neuts
• 金额: $ 12.16万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1986
• 资助国家: 美国
• 起止时间: 1986-08-15 至 1989-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8601203&HistoricalAwards=false
• 关键词: Algorithmic; Methods; Probability; Models

Probability models are used extensively throughout industry, government and defense, but have long been hampered by the high costs associated with the computations involved using standard methods. The area of research carried out by Professor Neuts focuses directly on these issues by the careful and innovative deviation and testing of efficient algorithms designed to lower these costs. New concepts introduced by Neuts include profile surfaces and curves which highlight data that are most significant in the model.

概率模型广泛应用于工业、政府、 但长期以来一直受到相关高成本的阻碍 使用标准方法进行计算。 领域 Neuts教授进行的研究直接集中在这些 问题的谨慎和创新的偏差和测试的效率 算法设计来降低这些成本。 引入的新概念 中性包括轮廓曲面和曲线，它们突出显示以下数据 模型中最重要的。