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Bayesian methods for the physical sciences: learning from examples in astronomy and physics

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 nu...

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Detalles Bibliográficos
Autores principales: Andreon, Stefano, Weaver, Brian
Lenguaje:eng
Publicado: Springer 2015
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-15287-5
http://cds.cern.ch/record/2019746
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author Andreon, Stefano
Weaver, Brian
author_facet Andreon, Stefano
Weaver, Brian
author_sort Andreon, Stefano
collection CERN
description 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 real-world 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.
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spelling cern-20197462021-04-21T20:18:01Zdoi:10.1007/978-3-319-15287-5http://cds.cern.ch/record/2019746engAndreon, StefanoWeaver, BrianBayesian methods for the physical sciences: learning from examples in astronomy and physicsMathematical Physics and MathematicsStatistical 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 real-world 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.Springeroai:cds.cern.ch:20197462015
spellingShingle Mathematical Physics and Mathematics
Andreon, Stefano
Weaver, Brian
Bayesian methods for the physical sciences: learning from examples in astronomy and physics
title Bayesian methods for the physical sciences: learning from examples in astronomy and physics
title_full Bayesian methods for the physical sciences: learning from examples in astronomy and physics
title_fullStr Bayesian methods for the physical sciences: learning from examples in astronomy and physics
title_full_unstemmed Bayesian methods for the physical sciences: learning from examples in astronomy and physics
title_short Bayesian methods for the physical sciences: learning from examples in astronomy and physics
title_sort bayesian methods for the physical sciences: learning from examples in astronomy and physics
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-319-15287-5
http://cds.cern.ch/record/2019746
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