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Modern Statistical Methods for Astronomy With R Applications [Hardcover]

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  • Category: Books (Science)
  • Author:  Feigelson, Eric D., Babu, G. Jogesh
  • Author:  Feigelson, Eric D., Babu, G. Jogesh
  • ISBN-10:  052176727X
  • ISBN-10:  052176727X
  • ISBN-13:  9780521767279
  • ISBN-13:  9780521767279
  • Publisher:  Cambridge University Press
  • Publisher:  Cambridge University Press
  • Pages:  490
  • Pages:  490
  • Binding:  Hardcover
  • Binding:  Hardcover
  • Pub Date:  01-May-2012
  • Pub Date:  01-May-2012
  • SKU:  052176727X-11-MPOD
  • SKU:  052176727X-11-MPOD
  • Item ID: 100835340
  • Seller: ShopSpell
  • Ships in: 2 business days
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  • Delivery by: Jul 01 to Jul 03
  • Notes: Brand New Book. Order Now.
A unique resource introducing graduate students and researchers in astronomy to advanced statistics through ready-to-use code.Linking astronomy to the world of modern statistics, this book is a unique resource introducing astronomers to advanced statistics through ready-to-use code in the public domain R statistical software environment. Examining fields of statistics applicable to astronomy, this is invaluable for graduate students and researchers facing complex data analysis tasks.Linking astronomy to the world of modern statistics, this book is a unique resource introducing astronomers to advanced statistics through ready-to-use code in the public domain R statistical software environment. Examining fields of statistics applicable to astronomy, this is invaluable for graduate students and researchers facing complex data analysis tasks.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.1. Introduction; 2. Probability; 3. Stals
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