TY  - BOOK
AU  - Ivezic, Zeljko.
AU  - Connolly,Andrew J.
AU  - VanderPlas,Jacob T.
AU  - Gray,Alexander
TI  - Statistics, data mining, and machine learning in astronomy: a practical Python guide for the analysis of survey data 
T2  - Princeton series in modern observational astronomy
SN  - 9780691151687
U1  - 522.85 23
PY  - 2014///
CY  - Princeton
PB  - Princeton University Press
KW  - Astronomy
KW  - Data processing
KW  - Statistical astronomy
N1  - 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--



N2  - 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
ER  -