000			03551cam a2200409 i 4500
001			21026112
003			OSt
005			20250306123800.0
008			190619t20202020njua b 001 0 eng
010			_a 2019022878
020			_a9780691198309 (hbk.)
040			_aPSt/DLC _beng _cJKRC _erda _dDLC
082	0	0	_a522.85 _223 _bIVE.Z
100	1		_aIvezic, Zeljko, _eAuthor. _931799
245	1	0	_aStatistics, data mining, and machine learning in astronomy : _ba practical Python guide for the analysis of survey data / _cby Zeljko Ivezic et. al... _hEnglish.
250			_a1st ed.
260			_aPrinceton : _bPrinceton University Press, _c2020.
264		4	_c©2020.
300			_ax, 537 p. _billustrations (some color) ; _c26 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	1		_aPrinceton series in modern observational astronomy
504			_aIncludes bibliographical references and index.
520			_a"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 indispensable reference for researchers. The updates in this new edition will include fixing "code rot," correcting errata, and adding some new sections. In particular, the new sections include new material on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) 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, evaluate the methods, and adapt them to their own fields of interest"-- _cProvided by publisher.
650		0	_aAstronomy _xData processing. _931800
650		0	_aStatistical astronomy. _931801
650		0	_aPython (Computer program language) _918289
700	1		_aConnolly, Andrew _q(Andrew J.), _eAuthor. _931802
700	1		_aVanderplas, Jacob T., _eAuthor. _931803
700	1		_aGray, Alexander _q(Alexander G.), _eAuthor. _931804
776	0	8	_iOnline version: _aIvezic, Zeljko, _tStatistics, data mining, and machine learning in astronomy _bUpdated edition. _dPrinceton : Princeton University Press, 2020. _z9780691197050 _w(DLC) 2019022879
830		0	_aPrinceton series in modern observational astronomy. _931805
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _c1 _e23 _n0
999			_c429220 _d429220