000			02750cam a2200301zu 4500
001			88838009
003			FRCYB88838009
005			20250107214942.0
006			m o d
007			cr un
008			250107s2014 fr | o|||||0|0|||eng d
020			_a9780691151687
035			_aFRCYB88838009
040			_aFR-PaCSA _ben _c _erda
100	1		_aIvezic, Zeljko
245	0	1	_aStatistics, Data Mining, and Machine Learning in Astronomy _bA Practical Python Guide for the Analysis of Survey Data _c['Ivezic, Zeljko', 'Connolly, Andrew J.', 'Vanderplas, Jacob T']
264		1	_bPrinceton University Press _c2014
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aIvezic, Zeljko
700	0		_aConnolly, Andrew J.
700	0		_aVanderplas, Jacob T
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88838009 _qtext/html _a
520			_aAs 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. 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. 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 contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers
999			_c64668 _d64668