Statistics, data mining, and machine learning in astronomy

Statistics, data mining, and machine learning in astronomy: a practical Python guide for the analysis of survey data

by Ivezić, Željko

Type Book
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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…

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Title Statements

  • Full title :Statistics, data mining, and machine learning in astronomy : a practical Python guide for the analysis of survey data / Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray.

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 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 indespensable reference for researchers.

More specifically, "this book is mostly about how to estimate the empirical pdf [probability density function] f(x) from data (including multidimensional cases), how to statistically describe the resulting estimate and its uncertainty, how to compare it to models specified via h(x) (including estimates of model parameters that describe h(x)), and how to use this knowledge to interpret additional and/or new measurements (including best-fit model reassessment and classification)." -- Print version, Page 8.

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Edition Statements

  • Publication Date:©2014

Publication Statements

The Physical Object

  • Description:1 online resource (x, 540 pages :) : illustrations.

Edition Identifiers

  • ISBN:9781400848911, 1400848911, 0691151687, 9780691151687, 9781306373845, 1306373840
  • oclc:on1084910048
  • LC Classification:QB149
  • Dewey Decimal:522/.8555133

Table of Contents

I. Introduction -- 1. About the Book and Supporting Material -- 1.1. What do Data Mining, Machine Learning, and Knowledge Discovery mean? -- 1.2. What is this book about? -- 1.3. An incomplete survey of the relevant literature -- 1.4. Introduction to the Python Language and the Git Code Management Tool -- 1.5. Description of surveys and data sets used in examples -- 1.6. Plotting and visualizing the data in this book -- 1.7. How to efficiently use this book -- References -- 2. Fast Computation on Massive Data Sets -- 2.1. Data types and Data Management systems -- 2.2. Analysis of algorithmic efficiency -- 2.3. Seven types of computational Problem[s] -- 2.4. Seven strategies for speeding things up -- 2.5. Case studies: Speedup strategies in practice -- References.

II. Statistical Frameworks and Exploratory Data Analysis -- 3. Probability and Statistical Distributions -- 3.1. Brief overview of probability and random variables -- 3.2. Descriptive statistics -- 3.3. Common Univariate Distribution Functions -- 3.4. The Central Limit Theorem -- 3.5. Bivariate and Multivariate Distribution Functions -- 3.6. Correlation coefficients -- 3.7. Random number generation for arbitrary distribu…

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Subjects

Astronomy -- Data processing Python (Computer program language) Python (Langage de programmation) statistical astronomy Statistique stellaire

Writers

Ivezić, Željko Connolly, Andrew, (Andrew J.) Gray, Alexander, (Alexander G.) Vanderplas, Jacob T.

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