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Statistics, Data Mining, and Machine Learning in Astronomy : A Practical Python Guide for the Analysis of Survey Data /
More specifically, "this book is mostly about how to estimate the empirical pdf [probability density function] f(x) from data (including multidimensional cases), how to statistically describe the resulting estimate and its uncertainty, how to compare it to models specified via h(x) (including e...
| Autores principales: | , , , |
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| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Princeton, N.J. :
Princeton University Press,
2014.
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| Colección: | Book collections on Project MUSE.
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| Temas: | |
| Acceso en línea: | Texto completo |
| Sumario: | More specifically, "this book is mostly about how to estimate the empirical pdf [probability density function] f(x) from data (including multidimensional cases), how to statistically describe the resulting estimate and its uncertainty, how to compare it to models specified via h(x) (including estimates of model parameters that describe h(x)), and how to use this knowledge to interpret additional and/or new measurements (including best-fit model reassessment and classification)."--Print version, Page 8 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 indespensable reference for researchers. |
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| Descripción Física: | 1 online resource: illustrations |
| ISBN: | 9781400848911 |