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Statistics, Data Mining, and Machine Learning in Astronomy is the
essential 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 Large Synoptic Survey Telescope. Now fully updated, it
presents a wealth of practical analysis problems, evaluates the
techniques for solving them, and explains how to use various
approaches for different types and sizes of data sets. Python code
and sample data sets are provided for all applications described in
the book. The supporting data sets have been carefully selected
from contemporary astronomical surveys 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, engage with the different methods, and adapt them to
their own fields of interest. An accessible textbook for students
and an indispensable reference for researchers, this updated
edition features new sections on deep learning methods,
hierarchical Bayes modeling, and approximate Bayesian computation.
The chapters have been revised throughout and the astroML code has
been brought completely up to date. Fully revised and expanded
Describes the most useful statistical and data-mining methods for
extracting knowledge from huge and complex astronomical data sets
Features real-world data sets from astronomical surveys Uses a
freely available Python codebase throughout Ideal for graduate
students, advanced undergraduates, and working astronomers
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