Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Updated Edition (Princeton Series in Modern Observational Astronomy)

Ivezi?, ?eljko; Connolly, Andrew J.; VanderPlas, Jacob T.; Gray, Alexander

ISBN 10: 0691198306 / ISBN 13: 9780691198309

Published by Princeton University Press, 2019

About this Item

Revised edition NO-PA16APR2015-KAP.

Seller Inventory # 26388148258

Bibliographic Details

• Title: Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Updated Edition (Princeton Series in Modern Observational Astronomy)
• Publisher: Princeton University Press
• Publication Date: 2019
• Language: English
• ISBN 10: 0691198306
• ISBN 13: 9780691198309
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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
  

About the Author

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.