Statistical learning: models and algorithms

统计学习：模型和算法

• 批准号: 203275-2010
• 负责人: Chipman, Hugh
• 金额: $ 1.46万
• 依托单位: Acadia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=477041
• 关键词: Statistical; learning; models; algorithms

Statistical scientists are being challenged to invent sophisticated models as a tool for discovery in problems with richly structured data. My research will attempt to bring core statistical ideas, such as accurate assessment of uncertainty, sequential design of experiments, mixed and mixture models, to bear on these challenging problems. Bayesian statistical methods will often be used, for their capacity to flexibly represent uncertainty and compute estimates using all available data.

统计科学家正面临着发明复杂模型的挑战，作为发现丰富结构化数据问题的工具。 我的研究将试图把核心的统计思想，如准确评估的不确定性，顺序设计的实验，混合和混合模型，承担这些具有挑战性的问题。 贝叶斯统计方法将经常使用，因为它们能够灵活地表示不确定性并使用所有可用数据计算估计数。