DiRAC faculty Zeljko Ivezic, together with DiRAC Institute Director Andy Connolly and collaborators Jacob Vanderplas and Alex Gray, have just published an updated edition of their book “Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data.”
The first edition of this book was published in 2012 by Princeton University Press and it has become a de facto standard for undergraduate and graduate courses, as well as for increasingly popular summer schools on Big Data in astronomy and astrophysics. It won the 2016 Outstanding Publication in Astrostatistics award from the International Astrostatistics Association.
This updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The methodology and examples in the book are supported by the publicly available astroML code written in python, and updated and expanded by Brigitta Sipocz, a DiRAC Fellow. The new edition was released on Dec 3, 2019 and is available from Amazon.
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.