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I延续了贝叶斯统计案例研究系列的传统。最初的系列研讨会是在奇数年的初秋在卡内基梅隆大学举行的。新研讨会的第一版将于2011年10月14-15日在卡内基梅隆大学举行。讲习班系列的最高目标是提出并提出各种领域内困难的实质性问题的成功解决办法。讲习班的具体目标是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展示应用导向研究的机会，这些研究改变了科学领域中分析数据的方式；3. 以科学上有用的方式激发对高维和复杂数据集分析挑战的讨论；4. 鼓励年轻的研究人员，包括研究生，展示他们的应用工作；5. 提供小型会议氛围，促进年轻研究人员与资深同事的互动；6. 让年轻研究人员接触合作研究中的重要挑战和机遇；7. 包括妇女、代表人数不足的少数民族和可能受益于小型讲习班环境的残疾人作为参与者；8. 鼓励通过文献完备和同行评议的期刊文章传播研讨会上的研究成果。

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.