- Additional Authors
- Description
- 1 online resource.
- Summary
- "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. The updates in this new edition will include fixing "code rot," correcting errata, and adding some new sections. In particular, the new sections include new material on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. 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"--
- Series Statement
- Princeton series in modern observational astronomy
- Uniform Title
- Statistics, data mining, and machine learning in astronomy (Online)
- Princeton series in modern observational astronomy.
- Alternative Title
- Statistics, data mining, and machine learning in astronomy (Online)
- Subject
- Bibliography (note)
- Includes bibliographical references and index.
- Access (note)
- Access restricted to authorized users.
- Source of Description (note)
- Description based on print version record and CIP data provided by publisher.
- LCCN
- 2019022879
- OCLC
- ssj0001190950
- Author
Ivezić, Željko.
- Title
Statistics, data mining, and machine learning in astronomy [electronic resource] : a practical Python guide for the analysis of survey data / Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas, Alexander Gray.
- Imprint
Princeton : Princeton University Press, [2020]
- Edition
Updated edition.
- Series
Princeton series in modern observational astronomy
Princeton series in modern observational astronomy.
- Bibliography
Includes bibliographical references and index.
- Access
Access restricted to authorized users.
- Note
Description based on print version record and CIP data provided by publisher.
- Connect to:
- Added Author
Connolly, Andrew (Andrew J.)
Vanderplas, Jacob T.
Gray, Alexander (Alexander G.)
- Other Form:
Print version: Ivezić, Željko. Statistics, data mining, and machine learning in astronomy Updated edition. Princeton : Princeton University Press, [2020] 9780691198309 (DLC) 2019022878