2014 Summer School in Statistics for Astronomers, June 2-13, 2014

2014年天文学统计十年暑期学校；2014年6月2-13日；宾夕法尼亚州立大学非常受欢迎的天文统计暑期学校自2005年成立以来，已经培训了超过575名参与者。如果保持稳定的状态，这项活动将培训大约10%的美国年轻天文学家，为美国的科学劳动力提供关键技能。面向研究生和年轻的研究人员，参与者接受高度熟练的讲师教授的统计方法的强烈沉浸。三管齐下的课程提供现代统计学基本原理的指导，接触天文学中有用的先进方法，以及统计软件的实践培训。与会者将在统计学及其在科学中的应用方面获得更高的专业知识，更好地理解如何将这些材料传授给他人，并对统计学在科学中的价值和意义有了更好的认识。教师们付出了相当大的努力，使课程材料适合他们的听众，课程也在不断发展。以该课程为基础的正式教材于2012年出版。天文学研究通常涉及对天空的成像、光度和光谱学调查，这些调查产生了万亿字节和千兆字节的数据库和十亿天体目录。测量科学是不久的将来的天文学，拥有新一代强大的望远镜和各种天文数据集的联盟。虽然前景很好，但实现科学目标关键取决于使用统计推断提取有用的知识，特别是使用先进的统计方法。因此，观测天文学家面临着比以往任何时候都更广泛的统计挑战，而不幸的是，大多数美国天文学家在统计方面没有受过良好的训练，他们只从物理学家撰写的或为物理学家撰写的书籍中学习基本的方法。这类卷通常只涉及范围狭窄的问题，在数理统计方面提供的概念基础不足，在高级应用统计方面的指导很少。统计是一种可用的技术，必须加以利用，以促进天文学和天体物理学的需要。2014年面向青年天文学家的统计推断暑期学校将使用经验丰富的教师和创新的课程，介绍中级水平的概念和方法。这是十年一次的课程，基于过去学校参与者的评论，2014年学校涵盖了为期两周的延长教学，分为三个独立的模块：第一周为天文学家提供常规统计推断课程，为期两天的“宇宙人口统计建模”补充课程，以及为期三天的“用于天文数据分析和贝叶斯计算的高性能计算”课程。宾夕法尼亚州立大学研究计算和网络基础设施组及其网络科学研究所提供技术人力并部分支持高性能计算。

2014 Decennial Summer School in Statistics for Astronomers; June 2-13 2014; Pennsylvania State University, University Park, State College, PAPenn State's very popular astrostatistics summer school has trained over 575 total participants since its inauguration in 2005. If maintained at a steady state, this activity will train about 10% of the nation's young astronomers, providing critical skills to the US scientific workforce. Oriented towards graduate students and young researchers, participants receive an intense immersion in statistical methodology taught by highly skilled instructors. The three-pronged curriculum provides instruction in the underlying principles of modern statistics, exposure to advanced methodologies useful in astronomy, and hands-on training in statistical software. Attendees come away with a much heightened expertise in statistics and its applications to their science, a better understanding of how to teach this material to others, and a fine appreciation for the value and meaning of statistics in science. The instructors have devoted considerable effort to adapting the course material to their audience, and the curriculum continues to evolve. The formal textbook based on this curriculum was published in 2012.Astronomical research often involves imaging, photometric and spectroscopic surveys of the sky that produce tera- and peta-byte databases and billion-object catalogs. Survey science is the astronomy of the near future, with a new generation of formidable telescopes and the federation of diverse astronomical datasets. While the promise is great, achieving the scientific goals depends critically on extraction of useful knowledge using statistical inference, and especially the use of advanced statistical methods. Observational astronomers are thus confronting a wider range of statistical challenges than ever before, while unfortunately most U.S. astronomers are not well trained in statistics, learning only elementary methods through books written by and for physical scientists. Such volumes usually treat only a narrow range of problems, providing inadequate conceptual foundations in mathematical statistics and little guidance in advanced applied statistics. Statistics is an available technology that must be tapped to advance the needs of astronomy and astrophysics.The 2014 Summer School in statistical inference for young astronomers will present concepts and methodologies at an intermediate level, using experienced instructors and an innovative curriculum. This being the decennial offering, and based on comments from the past school participants, the 2014 school covers a two-week extended period of instruction, divided into three independent modules: the regular statistical inference course for astronomers in the first week, a two-day supplement on 'Statistical modeling of cosmic populations', and a three-day school on 'High-Performance Computing for Astronomical Data Analysis & Bayesian Computing'. The Penn State Research Computing & Cyberinfrastructure Group, and its Institute for CyberScience, are providing technical manpower and partially supporting the high-performance computing.