Scientific Computing Research Environments for the Mathematical Sciences (SCREMS)

宾夕法尼亚州立大学统计系建议为统计科学建立一个计算环境，支持五个学院的研究项目。这些项目共享对高速计算的共同要求，应用程序包括天文学、图像分析、纵向数据分析、马尔可夫链蒙特卡罗(MCMC)算法、混合推理和环境科学。来自天文学、图像、生态学和地球科学的海量数据集需要高容量的磁盘存储，相应的分析极大地受益于快速、并行的计算资源。天文学方面的工作将增强和维护网络计算环境‘VOStat’，这将使天文学家能够轻松地对海量(TB级)数据进行各种统计分析。数字图片的自动标注是一项极具挑战性的技术，有着重要的应用前景。将开发使用复杂的统计分类技术来自动对图像进行分类的实时算法。这种技术可以在主要搜索引擎提供的图像搜索的基础上大大改进。纵向研究涉及在一段时间内对实验单位进行重复观察的数据，需要专门的统计方法。将为这类研究探索新的统计方法。混合模型是对复杂数据建模的非常灵活的方法。将在混合建模的背景下研究一些理论和实践问题，特别是在应用于海量数据集的情况下。MCMC算法是一种计算工具，用于将现实模型拟合到复杂数据中。将在地理上参考的数据模型的背景下开发和研究新的MCMC算法。这些计算密集的算法也将用于与宾夕法尼亚州立大学地理、生态和气候学的合作工作。

The Department of Statistics at Pennsylvania State University proposes the implementation of a computing environment for the statistical sciences, supporting research projects of five faculty.These projects share a common requirement for high-speed computing with applications including astronomy, image analysis, longitudinal data analysis, Markov Chain Monte Carlo (MCMC) algorithms, mixture inference and environmental science. Massive data sets from astronomy, images and ecology and geoscience require high-capacity disk storage and the corresponding analyses benefit greatly from fast, parallelized computing resources.The work in astronomy will enhance and maintain the web computing environment `VOStat', which will allow astronomers to easily conduct a variety of statistical analyses on massive (terabyte scale) data. Automated annotation of digital pictures is a highly challenging technology with significant applications. Real time algorithms will be developed that use sophisticated statistical classification techniques to automatically classify images. This kind of technology can greatly improve upon image searches provided by major search engines. Longitudinal studies involve data with repeated observations on experimental units over time and require specialized statistical methods. New statistical approaches will be investigated for such studies. Mixture models are very flexible ways to model complex data. A number of theoretical and practical issues will be investigated in the context of mixture modeling, particularly as applied to massive data sets. MCMC algorithms are computational tools for fitting realistic models to complex data. New MCMC algorithms will be developed and studied in the context of models for data that are geographically referenced. These computationally intensive algorithms will also be used in collaborative work with Geography,Ecology and Climatology at Penn State.