气候变化领域集成服务门户(CCPortal): Statistics, Data Mining, and Machine Learning in Astronomy

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	Statistics, Data Mining, and Machine Learning in Astronomy
其他题名	天文学的数据挖掘与机器学习：实践指南（精装）
	Ivezić Željko; Connolly; rew J.; V; erPlas Jacob T
出版日期	2014-01-05
出版者	Princeton University Press
国家	美国
摘要	As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) 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, evaluate the methods, and adapt them to their own fields of interest. 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 contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers
ISBN	9780691151687
所属丛书	Princeton Series in Modern Observational Astronomy
语种	英语
文献类型	专著
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/123074
推荐引用方式 GB/T 7714	Ivezić Željko,Connolly,rew J.,et al. Statistics, Data Mining, and Machine Learning in Astronomy[M]:Princeton University Press,2014.