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Eljko ivezi (5 risultati)

  • Lingua: Inglese

    Editore: Princeton University Press, 2014

    0691151687 / 9780691151687

    • Rilegato

    Da: World of Books (was SecondSale), Montgomery, IL, U.S.A.World of Books (was SecondSale)

    Venditore con 5 stelle
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    Condizione: Usato - Buono

    EUR 8,23

     Spedizione gratuita 
    Spedito in U.S.A.

    Quantità: 1 disponibile

    Hardback. Condizione: Good. 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.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 setsFeatures real-world data sets from contemporary astronomical surveysUses a freely available Python codebase throughoutIdeal for students and working astronomers.…

  • Lingua: Inglese

    Editore: Princeton University Press, 2014

    0691151687 / 9780691151687

    • Rilegato

    Da: World of Books Inc, Montgomery, IL, U.S.A.World of Books Inc

    Venditore con 4 stelle
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    Condizione: Usato - Buono

    EUR 10,06

     Spedizione gratuita 
    Spedito in U.S.A.

    Quantità: 1 disponibile

    Hardback. Condizione: Good. 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.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 setsFeatures real-world data sets from contemporary astronomical surveysUses a freely available Python codebase throughoutIdeal for students and working astronomers.…

  • Condizione: Usato - Molto buono

    EUR 65,59

     Spedizione gratuita 
    Spedito in U.S.A.

    Quantità: 1 disponibile

    hardcover. Condizione: Very Good. Complete and clean. All pages present and readable, binding intact. Highlighting or writing is absent or limited to a handful of pages. No water damage. Cover and spine may show light shelf wear, edge bumping, or minor creasing. May have a remainder mark, stickers or sticker residue, or a previous owner's name. Access codes, CDs, DVDs, and other bundled supplements may not be included and should be assumed used or missing unless the listing explicitly states otherwise.…

  • Lingua: Inglese

    Editore: Princeton University Press, 2019

    0691198306 / 9780691198309

    • Rilegato

    Da: Speedyhen, Hertfordshire, Regno UnitoSpeedyhen

    Venditore con 5 stelle
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    Condizione: Nuovo

    EUR 81,43

    EUR 49,13 spedizione 
    Spedito da Regno Unito a U.S.A.

    Quantità: 2 disponibili

    Condizione: NEW.

  • 0691198306 / 9780691198309

    Da: Basi6 International, Irving, TX, U.S.A.Basi6 International

    Venditore con 5 stelle
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    Condizione: Nuovo

    EUR 79,80

     Spedizione gratuita 
    Spedito in U.S.A.

    Quantità: 1 disponibile

    Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.