Statistics, data mining, and machine learning in astronomy : a practical Python guide for the analysis of survey data (Book, 2014) [WorldCat.org]
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Statistics, data mining, and machine learning in astronomy : a practical Python guide for the analysis of survey data

Author: Zeljko Ivezic; Andrew J Connolly, professeur d'astronomie).; Jacob T VanderPlas; Alexander Gray; et al
Publisher: Princeton (N.J.) ; Oxford : Princeton University Press. copyright 2014.
Series: Princeton series in modern observational Astronomy.
Edition/Format:   Print book : EnglishView all editions and formats
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  Read more...
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Genre/Form: Ressources Internet
Additional Physical Format: Statistics, data mining, and machine learning in astronomy : a practical Python guide for the analysis of survey data / Zeljko Ivezic, Andrew J. Connolly, Jacob T. VanderPlas... [et al.]
Princeton : Princeton University Press, 2014
978-1-400-84891-1
(ABES)187962308
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Zeljko Ivezic; Andrew J Connolly, professeur d'astronomie).; Jacob T VanderPlas; Alexander Gray; et al
ISBN: 9780691151687 0691151687
OCLC Number: 871317636
Description: 1 vol. (X-540 p.) : ill. en noir et en coul. ; 26 cm.
Contents: *Frontmatter, pg. i*Contents, pg. v*Preface, pg. ix*1. About the Book and Supporting Material, pg. 3*2. Fast Computation on Massive Data Sets, pg. 43*3. Probability and Statistical Distributions, pg. 69*4. Classical Statistical Inference, pg. 123*5. Bayesian Statistical Inference, pg. 175*6. Searching for Structure in Point Data, pg. 249*7. Dimensionality and Its Reduction, pg. 289*8. Regression and Model Fitting, pg. 321*9. Classification, pg. 365*10. Time Series Analysis, pg. 403*A. An Introduction to Scientific Computing with Python, pg. 471*B. AstroML: Machine Learning for Astronomy, pg. 511*C. Astronomical Flux Measurements and Magnitudes, pg. 515*D. SQL Query for Downloading SDSS Data, pg. 519*E. Approximating the Fourier Transform with the FFT, pg. 521*Visual Figure Index, pg. 527*Index, pg. 533
Series Title: Princeton series in modern observational Astronomy.
Responsibility: Zeljko Ivezic, Andrew J. Connolly, Jacob T. VanderPlas... [et al.].

Abstract:

Provides an 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  Read more...

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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 Read more...

 
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