Edité par Oxford University Press, 2014
ISBN 10 : 0691151687 ISBN 13 : 9780691151687
Langue: anglais
Vendeur : Labyrinth Books, Princeton, NJ, Etats-Unis
EUR 65,33
Autre deviseQuantité disponible : 13 disponible(s)
Ajouter au panierEtat : New.
Edité par Princeton University Press, 2019
ISBN 10 : 0691198306 ISBN 13 : 9780691198309
Langue: anglais
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 89,15
Autre deviseQuantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New. In.
Edité par Princeton University Press, 2019
ISBN 10 : 0691198306 ISBN 13 : 9780691198309
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 90,48
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierEtat : New.
Edité par Princeton University Press, 2019
ISBN 10 : 0691198306 ISBN 13 : 9780691198309
Langue: anglais
Vendeur : Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlande
EUR 97,84
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierEtat : New. 2019. Revised edition. Hardcover. . . . . .
Edité par Princeton University Press, 2019
ISBN 10 : 0691198306 ISBN 13 : 9780691198309
Langue: anglais
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 96,04
Autre deviseQuantité disponible : 3 disponible(s)
Ajouter au panierEtat : New.
Edité par Princeton University Press, 2019
ISBN 10 : 0691198306 ISBN 13 : 9780691198309
Langue: anglais
Vendeur : Books Puddle, New York, NY, Etats-Unis
EUR 104,44
Autre deviseQuantité disponible : 3 disponible(s)
Ajouter au panierEtat : New. Revised edition NO-PA16APR2015-KAP.
Edité par Princeton University Press, 2019
ISBN 10 : 0691198306 ISBN 13 : 9780691198309
Langue: anglais
Vendeur : Kennys Bookstore, Olney, MD, Etats-Unis
EUR 119,85
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierEtat : New. 2019. Revised edition. Hardcover. . . . . . Books ship from the US and Ireland.
Edité par Princeton University Press, New Jersey, 2019
ISBN 10 : 0691198306 ISBN 13 : 9780691198309
Langue: anglais
Vendeur : CitiRetail, Stevenage, Royaume-Uni
EUR 92,21
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierHardcover. Etat : new. Hardcover. Statistics, Data Mining, and Machine Learning in Astronomy is the 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. Now 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.An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.Fully revised and expandedDescribes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data setsFeatures real-world data sets from astronomical surveysUses a freely available Python codebase throughoutIdeal for graduate students, advanced undergraduates, and working astronomers Statistics, Data Mining, and Machine Learning in Astronomy is the 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. Now fully updated, it presents a wealth o Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Edité par Princeton University Press, New Jersey, 2019
ISBN 10 : 0691198306 ISBN 13 : 9780691198309
Langue: anglais
Vendeur : Grand Eagle Retail, Fairfield, OH, Etats-Unis
EUR 106,24
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierHardcover. Etat : new. Hardcover. Statistics, Data Mining, and Machine Learning in Astronomy is the 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. Now 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.An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.Fully revised and expandedDescribes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data setsFeatures real-world data sets from astronomical surveysUses a freely available Python codebase throughoutIdeal for graduate students, advanced undergraduates, and working astronomers Statistics, Data Mining, and Machine Learning in Astronomy is the 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. Now fully updated, it presents a wealth o Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Edité par Princeton University Press, New Jersey, 2019
ISBN 10 : 0691198306 ISBN 13 : 9780691198309
Langue: anglais
Vendeur : AussieBookSeller, Truganina, VIC, Australie
EUR 158,23
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierHardcover. Etat : new. Hardcover. Statistics, Data Mining, and Machine Learning in Astronomy is the 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. Now 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.An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.Fully revised and expandedDescribes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data setsFeatures real-world data sets from astronomical surveysUses a freely available Python codebase throughoutIdeal for graduate students, advanced undergraduates, and working astronomers Statistics, Data Mining, and Machine Learning in Astronomy is the 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. Now fully updated, it presents a wealth o Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.