CASE STUDIES IN BAYESIAN STATISTICS AND MACHINE LEARNING

描述（由申请人提供）：贝叶斯统计和机器学习案例研究我继续在贝叶斯统计系列案例研究的传统。最初的一系列研讨会是在奇数年在卡内基梅隆大学举行的初秋。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举行。系列研讨会的最高目标是为各个领域的困难实质性问题制定并提出成功的解决方案。研讨会的具体目标是1.提出并讨论具有挑战性的科学问题的解决方案，说明统计机器学习方法在实质性研究中的潜力; 2.为统计学家和计算机科学家提供了一个机会，可以展示以应用为导向的研究，改变科学领域中数据分析的方式; 3.鼓励讨论以科学有用的方式分析高维和复杂数据集的挑战; 4.鼓励年轻的研究人员，包括研究生，介绍他们的应用工作; 5。提供一个小型的会议氛围，促进年轻研究人员与资深同事的互动; 6.让年轻的研究人员在合作研究中面临重要的挑战和机遇; 7.让可能受益于小型讲习班环境的妇女、代表人数不足的少数群体和残疾人参加;鼓励通过文献充分和同行评审的期刊文章传播研讨会上提出的研究结果。 公共卫生相关性：贝叶斯和统计机器学习方法对于健康科学中的数据分析至关重要，特别是在癌症等复杂疾病中。拟议的研讨会将突出贝叶斯和统计机器学习的有趣应用，特别是在与癌症研究相关的生物信息学和成像领域，并为统计学，计算机科学和其他学科的初级和高级研究人员提供重要合作的场所。

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.