Case Studies in Bayesian Statistics and Machine Learning Workshop Conference Travel; October 2009, Pittsburgh, PA

贝叶斯统计和机器学习研讨会会议旅行案例研究；

• 批准号: 0939609
• 负责人: Joseph Kadane
• 金额: $ 0.5万
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0939609&HistoricalAwards=false
• 关键词: Case; Studies; Bayesian; Statistics; Machine

The First Workshop in Bayesian Statistics and Machine Learning, to be held at Carnegie Mellon University, October 15-17, 2009, will hear extended discussion of the following case studies: "Decision theoretic Bayesian nonparametric inference for the molecular characterization and stratification of colorectal cancer using genome-wide arrays", by Christopher C. Holmes, Christopher Yau, Ian Tomlinson and Jean-Baptiste Cazier; "Calibrating the Universe: A Bayesian Uncertainty Analysis of Galaxy Simulation" by Ian Vernon, Richard Bower and Michael Goldstein; "Rigorous Error Analysis for Small Angle Neutron Scattering Datasets Using Bayesian Inference", by Chip Hogg, Jay Kadane, Jong Soo Lee, and Sara Majetich. A new investigator's session, a short-course introduction to Bayesian analysis and machine learning, a session on how to get a grant, and a mixer, are all aimed at attracting students and recent graduates. This grant supports travel and local expenses for speakers and young investigators to attend the First Workshop in Bayesian Statistics and Machine Learning, to be held at Carnegie Mellon University, October 15-17, 2009. The fields of Bayesian statistics and machine learning are starting to grow together; we hope that the workshop will accelerate this process by focusing attention on serious scientific applications of the methods.

第一届贝叶斯统计和机器学习研讨会将于2009年10月15日至17日在卡内基梅隆大学举行，届时将听取对下列案例研究的深入讨论：Christopher C.Holmes、Christopher Yau、Ian Tomlinson和Jean-Baptiste Cazier所作的“使用全基因组阵列对结直肠癌分子特征和分层的决策理论贝叶斯非参数推断”；Ian Vernon、Richard Bower和Michael Goldstein所作的“校准宇宙：星系模拟的贝叶斯不确定性分析”；Chip Hogg、Jay Kadane、Jong Soo Lee和Sara Majetich所作的“使用贝叶斯推理对小角中子散射数据集进行严格的误差分析”。一个新的调查员课程，一个贝叶斯分析和机器学习的短期课程，一个关于如何获得资助的课程，以及一个混合会，所有这些都是为了吸引学生和新近毕业的学生。这笔赠款支持演讲者和年轻研究人员参加将于2009年10月15日至17日在卡内基梅隆大学举行的贝叶斯统计和机器学习第一次研讨会的旅费和当地费用。贝叶斯统计和机器学习领域正开始共同发展；我们希望讲习班将重点关注这些方法的严肃科学应用，从而加速这一进程。