{"title":"Series: Princeton Series in Modern Observational Astronomy","description":"\u003cp\u003eThe \u003cstrong\u003ePrinceton Series in Modern Observational Astronomy\u003c\/strong\u003e explores the latest advancements and methodologies in the study of the cosmos. Readers can expect in-depth discussions that blend rigorous scientific inquiry with accessible explanations, offering insights into the tools and techniques that shape our understanding of the universe.\u003c\/p\u003e\n\n\u003cp\u003eIdeal for enthusiasts and scholars alike, this series bridges the gap between theoretical concepts and practical observation, inviting a thoughtful engagement with the mysteries of the night sky. It embodies a commitment to clarity and precision, making complex astronomical phenomena approachable without sacrificing depth.\u003c\/p\u003e","products":[{"product_id":"statistics-data-mining-and-machine-learning-in-astronomy-by-zeljko-ivezic-9780691198309","title":"Statistics, Data Mining, and Machine Learning in Astronomy","description":"\u003cdiv class=\"book-description\"\u003e\n\u003cp\u003e\u003cem\u003eStatistics, Data Mining, and Machine Learning in Astronomy\u003c\/em\u003e is the essential introduction to the statistical methods needed to analyse 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.\u003c\/p\u003e\n\n\u003cp\u003eNow 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.\u003c\/p\u003e\n\n\u003cp\u003eAn accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modelling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.\u003c\/p\u003e\n\n\u003cp\u003eFully revised and expanded, it describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets, includes real-world data sets from astronomical surveys, and uses a freely available Python codebase throughout. It is ideal for graduate students, advanced undergraduates, and working astronomers.\u003c\/p\u003e\n\u003c\/div\u003e","brand":"NewSouth Books","offers":[{"title":"Default Title","offer_id":47455739674860,"sku":"9780691198309","price":216.0,"currency_code":"NZD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0705\/7784\/8556\/files\/71txTe-BakL._SL1500.jpg?v=1774795168"}],"url":"https:\/\/bookhero.co.nz\/collections\/series-princeton-series-in-modern-observational-astronomy.oembed","provider":"Book Hero","version":"1.0","type":"link"}