Bayesian methods for the physical sciences : learning from examples in astronomy and physics
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The work Bayesian methods for the physical sciences : learning from examples in astronomy and physics represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
The Resource
Bayesian methods for the physical sciences : learning from examples in astronomy and physics
Resource Information
The work Bayesian methods for the physical sciences : learning from examples in astronomy and physics represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 Bayesian methods for the physical sciences : learning from examples in astronomy and physics
 Title remainder
 learning from examples in astronomy and physics
 Statement of responsibility
 Stefano Andreon, Brian Weaver
 Subject

 Theoretical & mathematical astronomy
 Statistics
 Electronic books
 Bayesian statistical decision theory
 Mathematical physics
 MATHEMATICS  Probability & Statistics  General
 Mathematical physics
 Mathematical physics
 Astronomy, Astrophysics and Cosmology
 MATHEMATICS  Applied
 Probability & statistics
 Statistical astronomy
 Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
 Mathematical Methods in Physics
 Statistical astronomy
 Bayesian statistical decision theory
 Language
 eng
 Summary
 Statistical literacy is critical for the modern researcher in Physics and Astronomy. This book empowers researchers in these disciplines by providing the tools they will need to analyze their own data. Chapters in this book provide a statistical base from which to approach new problems, including numerical advice and a profusion of examples. The examples are engaging analyses of realworld problems taken from modern astronomical research. The examples are intended to be starting points for readers as they learn to approach their own data and research questions. Acknowledging that scientific progress now hinges on the availability of data and the possibility to improve previous analyses, data and code are distributed throughout the book. The JAGS symbolic language used throughout the book makes it easy to perform Bayesian analysis and is particularly valuable as readers may use it in a myriad of scenarios through slight modifications. This book is comprehensive, well written, and will surely be regarded as a standard text in both astrostatistics and physical statistics. Joseph M. Hilbe, President, International Astrostatistics Association, Professor Emeritus, University of Hawaii, and Adjunct Professor of Statistics, Arizona State University
 Cataloging source
 N$T
 Dewey number
 519.5/42
 Illustrations
 illustrations
 Index
 no index present
 LC call number
 QC20.7.B38
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 Series statement
 Springer series in astrostatistics,
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