Overview

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 o

Full Product Details

Author:   Zeljko Ivezic ,  Andrew J. Connolly ,  Jacob T. VanderPlas ,  Alexander Gray
Publisher:   Princeton University Press
Imprint:   Princeton University Press
Edition:   Revised edition
Volume:   8
ISBN:  

9780691198309

ISBN 10:   0691198306
Pages:   560
Publication Date:   03 December 2019
Audience:   General/trade ,  College/higher education ,  General/trade ,  General ,  Tertiary & Higher Education
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.
Language:   English

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Reviews

"Praise for the previous edition: ""A comprehensive, accessible, well-thought-out introduction to the new and burgeoning field of astrostatistics.""—Choice ""A substantial work that can be of value to students and scientists interested in mining the vast amount of astronomical data collected to date. . . . If data mining and machine learning fall within your interest area, this text deserves a place on your shelf.""—Planetarian ""This comprehensive book is surely going to be regarded as one of the foremost texts in the new discipline of astrostatistics.""—Joseph M. Hilbe, president of the International Astrostatistics Association ""In the era of data-driven science, many students and researchers have faced a barrier to entry. Until now, they have lacked an effective tutorial introduction to the array of tools and code for data mining and statistical analysis. The comprehensive overview of techniques provided in this book, accompanied by a Python toolbox, free readers to explore and analyze the data rather than reinvent the wheel.""—Tony Tyson, University of California, Davis ""The authors are leading experts in the field who have utilized the techniques described here in their own very successful research. Statistics, Data Mining, and Machine Learning in Astronomy is a book that will become a key resource for the astronomy community.""—Robert J. Hanisch, Space Telescope Science Institute"

Praise for the previous edition: A comprehensive, accessible, well-thought-out introduction to the new and burgeoning field of astrostatistics. -Choice A substantial work that can be of value to students and scientists interested in mining the vast amount of astronomical data collected to date. . . . If data mining and machine learning fall within your interest area, this text deserves a place on your shelf. -Planetarian This comprehensive book is surely going to be regarded as one of the foremost texts in the new discipline of astrostatistics. -Joseph M. Hilbe, president of the International Astrostatistics Association In the era of data-driven science, many students and researchers have faced a barrier to entry. Until now, they have lacked an effective tutorial introduction to the array of tools and code for data mining and statistical analysis. The comprehensive overview of techniques provided in this book, accompanied by a Python toolbox, free readers to explore and analyze the data rather than reinvent the wheel. -Tony Tyson, University of California, Davis The authors are leading experts in the field who have utilized the techniques described here in their own very successful research. Statistics, Data Mining, and Machine Learning in Astronomy is a book that will become a key resource for the astronomy community. -Robert J. Hanisch, Space Telescope Science Institute

Author Information

Željko Ivezić is professor of astronomy at the University of Washington. Andrew J. Connolly is professor of astronomy at the University of Washington. Jacob T. VanderPlas is a software engineer at Google. Alexander Gray is vice president of AI science at IBM.

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