Scalable Bayesian model selection for massive data sets

海量数据集的可扩展贝叶斯模型选择

• 批准号: DE130101670
• 负责人: A/Prof John Ormerod
• 金额: $ 27.03万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2013
• 资助国家: 澳大利亚
• 起止时间: 2013-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE130101670
• 关键词: Scalable; Bayesian; model; selection; massive

This project will develop highly innovative, efficient and ultimately effective methodology for Bayesian model selection for large-scale problems which commonly arise in biostatistics and bioinformatics. The resulting methodology will dramatically reduce the duration of analyses in these areas from days or weeks to minutes or hours.

该项目将为生物统计学和生物信息学中常见的大规模问题开发高度创新，高效和最终有效的贝叶斯模型选择方法。由此产生的方法将大大减少这些领域的分析时间，从几天或几周减少到几分钟或几小时。