Statistics, Data Mining, and Machine Learning in Astronomy
Statistics, Data Mining, and Machine Learning in Astronomy
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DOI：
10.2307/j.ctvrxk1hs
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Ž. Ivezić;A. Connolly;J. Vanderplas;Alexander Gray
中科院分区：
文献类型：
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作者：
Ž. Ivezić;A. Connolly;J. Vanderplas;Alexander Gray
天文学中的统计、数据挖掘和机器学习是分析全景巡天望远镜和快速响应系统、暗能量巡天和大型综合巡天望远镜等天文巡天复杂数据集所需的统计方法的重要介绍。现在已全面更新，它提出了丰富的实际分析问题，评估了解决这些问题的技术，并解释了如何针对不同类型和大小的数据集使用各种方法。本书中描述的所有应用程序都提供了Python代码和示例数据集。支持数据集是从当代天文调查中精心挑选的，易于下载和使用。随附的 Python 代码是公开可用的、有详细记录并遵循统一的编码标准。数据集和代码一起使读者能够重现所有的图形和示例，使用不同的方法，并将其适应自己感兴趣的领域。该更新版是一本适合学生使用的教科书，也是研究人员不可或缺的参考资料，其中包含有关深度学习方法、分层贝叶斯建模和近似贝叶斯计算的新章节。这些章节已经过全面修订，并且 astroML 代码已完全更新。全面修订和扩展 描述了从庞大而复杂的天文数据集中提取知识的最有用的统计和数据挖掘方法 具有来自天文调查的真实世界数据集 全文使用免费提供的 Python 代码库 非常适合研究生、高年级本科生和在职天文学家
Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest. An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date. Fully revised and expanded Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from astronomical surveys Uses a freely available Python codebase throughout Ideal for graduate students, advanced undergraduates, and working astronomers