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- Sales Rank: #225248 in Books
- Brand: Brand: Princeton University Press
- Published on: 2014-01-12
- Original language: English
- Number of items: 1
- Dimensions: 10.00" h x 7.00" w x 1.75" l, 2.75 pounds
- Binding: Hardcover
- 552 pages
- Used Book in Good Condition
Review
Winner of the 2016 IAA Outstanding Publication Award, International Astrostatistics Association
"Ivezic and colleagues at the University of Washington and the Georgia Institute of Technology have written a comprehensive, accessible, well-thought-out introduction to the new and burgeoning field of astrostatistics. . . . The authors provide another valuable service by discussing how to access data from key astronomical research programs."--Choice
From the Back Cover
"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
About the Author
Željko Ivezi? 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 an NSF postdoctoral research fellow in astronomy and computer science at the University of Washington. Alexander Gray is professor of computer science at Georgia Institute of Technology.
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