Statistics, Data Mining, and Machine Learning in Astronomy
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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
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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. Les mer
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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
Detaljer
- Forlag
- Princeton University Press
- Innbinding
- Innbundet
- Språk
- Engelsk
- Sider
- 560
- ISBN
- 9780691198309
- Utgivelsesår
- 2019
- Format
- 25 x 18 cm
Anmeldelser
«
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
»