CASE STUDIES IN BAYESIAN STATISTICS AND MACHINE LEARNING

贝叶斯统计和机器学习的案例研究

• 批准号: 1114922
• 负责人: Robert Kass
• 金额: $ 1.5万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1114922&HistoricalAwards=false
• 关键词: CASE; STUDIES; BAYESIAN; STATISTICS; MACHINE

Case Studies in Bayesian Statistics and Machine Learning continues the tradition of the workshop series Case Studies in Bayesian Statistics with a meeting October 14-15, 2011. The usual format for meetings and workshops in statistics and computer science emphasize methods over applications, which often stifles discussion about the impact of the methods on the substantive problem. The unique format of the case studies workshop series has allowed for substantive discussion of application-specific issues, most importantly a narrative of how the substantive scientific problem demanded either new methods or the novel application of existing approaches, the obstacles in the real problem that the researchers encountered, and the solutions that resulted. Statistics and machine learning provide essential methodologies throughout the sciences, yet the connection between the science and the data analytic problem formulation is rarely emphasized in traditional conferences. This workshop fosters cross-pollination of ideas from the statistics and computer science ommunities and promotes work that takes on important challenges in scientific investigation. The case studies highlight the way novel application of statistical machine learning methods are used to answer a scientific question. The workshop especially supports efforts by young investigators, in part by including a session of presentations exclusively by younger investigators.

贝叶斯统计和机器学习案例研究延续了贝叶斯统计案例研究系列研讨会的传统，于2011年10月14日至15日举行会议。统计和计算机科学会议和讲习班的通常形式强调方法而不是应用，这往往扼杀了关于方法对实质性问题的影响的讨论。案例研究讲习班系列的独特形式使人们能够对具体应用问题进行实质性讨论，最重要的是叙述实质性科学问题如何需要新方法或现有方法的新应用，研究人员遇到的真实的问题中的障碍以及由此产生的解决方案。 统计学和机器学习为整个科学提供了基本的方法论，但在传统的会议中很少强调科学与数据分析问题制定之间的联系。该研讨会促进了统计和计算机科学领域思想的交流，并促进了在科学调查中面临重要挑战的工作。 案例研究突出了统计机器学习方法的新应用用于回答科学问题的方式。讲习班特别支持年轻调查人员的工作，包括专门由年轻调查人员介绍情况的一次会议。