Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Updated Edition Zeljko Ivezic, Andrew J. Connolly, and Jacob T. VanderPlas are an English-language textbook for higher education and professionals in astronomy.
This reference work covers statistical methods, data mining, and machine learning for the analysis of complex data sets from astronomical surveys such as Pan-STARRS, the Dark Energy Survey, and the Large Synoptic Survey Telescope. The fully updated edition includes practical examples, Python code, and downloadable data sets that enable users to perform reproducible analyses. The content has been expanded with new topics including deep learning, hierarchical Bayesian models, and approximate Bayesian computations.
Features:
- In-depth treatment of statistics, machine learning, and data mining in astronomy
- Use of current and accessible astronomical data sets
- Practical Python code that is consistently documented and freely available
- Suitable as a reference for scientists and as a textbook for students
This textbook connects with fields such as theoretical and mathematical astronomy and astrophysics.
Series: Princeton Series in Modern Observational Astronomy