High-performance Computational Bayesian Inference for Astronomy

贝叶斯分析能够有效地组合来自不同来源的信息，有助于为模型选择和假设检验提供理论模型和方法的置信度。这个项目将最先进的统计和计算算法应用于天文学中的一组数据和理论比较。这本书涉及其他科学中的各种问题，有助于满足对优化数据挖掘工具的需求。该系统可以远程、协同地进行控制和操作。持久性系统的引入将允许保存数据和元数据，使其可供将来使用。统计方法最有效地利用了所有可用的信息，并允许直接检验假设。贝叶斯统计模型在整合和使用所有信息方面提供了最大的灵活性，形成了一个虚拟的天文台试验台。为研究生提供多学科培训机会，并在会议、培训班和通过下载向个人用户传播工作。

Bayesian analysis has the power to combine information efficiently from diverse sources, helping to provide confidence bounds on theoretical models and methods for both model selection and hypothesis testing. This project brings state-of-the-art statistical and computational algorithms to bear on a set of data and theory comparison in astronomy. The wok bears on a variety of problems in other sciences, helping to fill the need for optimized data-mining tools. The system can be controlled and operated remotely and collaboratively. The introduction of a persistence system will permit saving of both data and metadata, making it accessible for future use. The statistical approach make the most efficient use of all the available information and permit the testing of hypotheses directly. Bayesian statistical models provide maximum flexibility in incorporating and using all the information, making a virtual observatory test-bed. Multi-disciplinary training opportunities are provided for graduate students and the work disseminated at both meetings, training sessions and for individual users through downloads.