Modern Statistical Methods for Astronomy

With R Applications

Nonfiction, Science & Nature, Science, Physics, Astronomy, Mathematics
Cover of the book Modern Statistical Methods for Astronomy by Eric D. Feigelson, G. Jogesh Babu, Cambridge University Press
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Author: Eric D. Feigelson, G. Jogesh Babu ISBN: 9781107713437
Publisher: Cambridge University Press Publication: July 12, 2012
Imprint: Cambridge University Press Language: English
Author: Eric D. Feigelson, G. Jogesh Babu
ISBN: 9781107713437
Publisher: Cambridge University Press
Publication: July 12, 2012
Imprint: Cambridge University Press
Language: English

Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public domain R statistical software environment. The book presents fundamental results of probability theory and statistical inference, before exploring several fields of applied statistics, such as data smoothing, regression, multivariate analysis and classification, treatment of nondetections, time series analysis, and spatial point processes. It applies the methods discussed to contemporary astronomical research datasets using the R statistical software, making it invaluable for graduate students and researchers facing complex data analysis tasks. A link to the author's website for this book can be found at www.cambridge.org/msma. Material available on their website includes datasets, R code and errata.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public domain R statistical software environment. The book presents fundamental results of probability theory and statistical inference, before exploring several fields of applied statistics, such as data smoothing, regression, multivariate analysis and classification, treatment of nondetections, time series analysis, and spatial point processes. It applies the methods discussed to contemporary astronomical research datasets using the R statistical software, making it invaluable for graduate students and researchers facing complex data analysis tasks. A link to the author's website for this book can be found at www.cambridge.org/msma. Material available on their website includes datasets, R code and errata.

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