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Statistics, Data Mining, and Machine Learning in Astronomy [Elektronisk resurs] a practical python guide for the analysis of survey data / Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas, Alexander Gray.

Ivezić, Željko (författare)
Ivezić, Željko (författare)
Connolly, Andrew J. (författare)
VanderPlas, Jacob T. (författare)
Gray, Alexander (författare)
ISBN 9781400848911
Publicerad: Princeton : Princeton University Press, 2014
Engelska online resource (x, 540 sidor)
Serie: Princeton series in modern observational astronomy
Läs hela texten (Table of contents)
  • E-bok
Innehållsförteckning Sammanfattning Ämnesord
  • 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. 
  • 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 the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) 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, evaluate the methods, and adapt them to their own fields of interest. 


Python (Computer program language)  (LCSH)
Statistical astronomy  (LCSH)
Data mining  (LCSH)
Machine learning  (LCSH)
Astronomy  -- Data processing (LCSH)
COMPUTERS / Databases / Data Mining 
COMPUTERS / Intelligence (AI) & Semantics 
MATHEMATICS / Probability & Statistics / General 
SCIENCE / Astronomy 
SCIENCE / Physics / Astrophysics 
SCIENCE / Physics / Mathematical & Computational 


Electronic books 


006.312 (DDC)
Pud (kssb/8 (machine generated))
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