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.

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.