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Statistics, data mining, and machine learning in astronomy : a practical Python guide for the analysis of survey data / Zeljko Ivezic...[et al.].

By: Contributor(s): Material type: TextTextSeries: Princeton series in modern observational astronomyPublication details: Princeton : Princeton University Press, c2014.Description: x, 540 p. : illustrations ; 29 cmISBN:
  • 9780691151687
Subject(s): DDC classification:
  • 23 Iv95 522.85
Contents:
I. Introduction-- 1. About the Book and Supporting Material-- 2. Fast Computation on Massive Data Sets-- II. Statistical Frameworks and Exploratory Data Analysis-- 3. Probability and Statistical Distributions-- 4. Classical Statistical Inference-- 5. Bayesian Statistical Inference-- III. Data Mining and Machine Learning-- 6. Searching for Structure in Point Data-- 7. Dimensionality and Its Reduction-- 8. Regression and Model fitting-- 9. Classification-- 10. Time Series Analysis-- IV. Appendices-- A. An Introduction to Scientific Computing with Python-- B. AstroML: Machine Learning for Astronomy-- C. Astronomical Flux Measurements and Magnitudes-- D. SQL Query for Downloading SDSS Data E. Approximating the Fourier Transform with the FFT-- References-- Visual Figure Index-- Index--
Summary: 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 Survey, and the upcoming Large Synoptic Survey Telescope.
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Includes bibliographical references and index.

I. Introduction--
1. About the Book and Supporting Material--
2. Fast Computation on Massive Data Sets--

II. Statistical Frameworks and Exploratory Data Analysis--
3. Probability and Statistical Distributions--
4. Classical Statistical Inference--
5. Bayesian Statistical Inference--

III. Data Mining and Machine Learning--
6. Searching for Structure in Point Data--
7. Dimensionality and Its Reduction--
8. Regression and Model fitting--
9. Classification--
10. Time Series Analysis--

IV. Appendices--
A. An Introduction to Scientific Computing with Python--
B. AstroML: Machine Learning for Astronomy--
C. Astronomical Flux Measurements and Magnitudes--
D. SQL Query for Downloading SDSS Data
E. Approximating the Fourier Transform with the FFT--

References--
Visual Figure Index--
Index--


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 Survey, and the upcoming Large Synoptic Survey Telescope.

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