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: 8203089
• 负责人: ROBERT E KASS
• 金额: $ 1.25万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-18 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8203089
• 关键词: Area; Bioinformatics; Case Study; Collaborations; Communities; Complex; Computers; Data Analyses; Data Set; Disabled Persons; Discipline; Disease; Educational workshop; Environment; Fostering; Goals; Hand; Health Sciences; Image; Institutes; Machine Learning; Malignant Neoplasms; Methodology; Methods; National Human Genome Research Institute; Participant; Peer Review; Research; Research Personnel; Scientist; Series; Solutions; Underrepresented Minority; Universities; Woman; Work; anticancer research; computer science; data modeling; falls; graduate student; interest; journal article; meetings; peer; planetary Atmosphere; statistics; symposium

DESCRIPTION (provided by applicant): Case Studies in Bayesian Statistics and Machine Learning I continues in the tradition of the Case Studies in Bayesian Statistics series. The original series of workshops were held in odd years at Carnegie Mellon University in the early fall. The first edition of the new workshop will be held at Carnegie Mellon University on October 14-15, 2011. The highest level goal of the workshop series is to generate and present successful solutions to difficult substantive problems in a wide variety of areas. The specific objectives of the workshop are to 1. Present and discuss solutions to challenging scientific problems that illustrate the potential for statistical machine learning approaches in substantive research; 2. Present an opportunity for statisticians and computer scientists to present applications-oriented research that changes the way that data are analyzed in scientific fields; 3. Stimulate discussion of the challenges of the analysis of high-dimensional and complex datasets in a scientifically useful manner; 4. Encourage young researchers, including graduate students, to present their applied work; 5. Provide a small meeting atmosphere to facilitate the interaction of young researchers with senior colleagues; 6. Expose young researchers to important challenges and opportunities in collaborative research; 7. Include as participants women, under-represented minorities and persons with disabilities who might benefit from the small workshop environment; 8. Encourage dissemination of the findings presented at the workshop via well-documented and peer- reviewed journal articles. PUBLIC HEALTH RELEVANCE: Bayesian and statistical machine learning approaches are essential for the analysis of data in the health sciences, particularly in complex diseases like cancer. The proposed workshop will highlight interesting applications of Bayesian and statistical machine learning, particularly in bioinformatics and imaging, which are relevant to cancer research and provide a venue for important collaboration amongst junior and senior researchers in statistics, computer science, and other disciplines.

描述（由申请人提供）：贝叶斯统计和机器学习中的案例研究我在贝叶斯统计系列中的案例研究的传统中继续进行。最初的一系列研讨会是在秋天在卡内基·梅隆大学（Carnegie Mellon University）举行的。新研讨会的第一版将于2011年10月14日至15日在卡内基·梅隆大学（Carnegie Mellon University）举行。研讨会系列的最高水平目标是为各种领域的困难实质性问题提供成功的解决方案。研讨会的具体目标是至1。就挑战性科学问题提供了解决方案，这些问题说明了实质性研究中统计机器学习方法的潜力； 2。为统计学家和计算机科学家提供了一个以应用程序为导向的研究的机会，该研究改变了在科学领域分析数据的方式； 3.刺激以科学有用的方式对高维和复杂数据集分析的挑战的讨论； 4。鼓励包括研究生在内的年轻研究人员介绍他们的应用工作； 5。提供一个小的聚会氛围，以促进年轻研究人员与高级同事的相互作用； 6。使年轻研究人员面临合作研究中的重要挑战和机遇； 7。包括作为参与者，代表性不足的少数群体和可能从小型研讨会环境中受益的残疾人； 8.鼓励通过有据可查的和同行评审的期刊文章在研讨会上介绍的调查结果传播。 公共卫生相关性：贝叶斯和统计机器学习方法对于分析健康科学的数据至关重要，尤其是在癌症等复杂疾病中。拟议的研讨会将重点介绍贝叶斯和统计机器学习的有趣应用，尤其是在与癌症研究有关的生物信息学和成像中，并为统计，计算机科学和其他学科的初级和高级研究人员之间的重要合作提供了一个重要的场所。