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Bayesian Reasoning in Data Analysis: A Critical Introduction
This book provides a multi-level introduction to Bayesian reasoning (as opposed to "conventional statistics") and its applications to data analysis. The basic ideas of this "new" approach to the quantification of uncertainty are presented using examples from research and everyday...
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Lenguaje: | eng |
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World Scientific
2003
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Acceso en línea: | https://dx.doi.org/10.1142/5262 http://cds.cern.ch/record/642515 |
_version_ | 1780900750266728448 |
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author | D'Agostini, Giulio |
author_facet | D'Agostini, Giulio |
author_sort | D'Agostini, Giulio |
collection | CERN |
description | This book provides a multi-level introduction to Bayesian reasoning (as opposed to "conventional statistics") and its applications to data analysis. The basic ideas of this "new" approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference; combination of results; treatment of uncertainty due to systematic errors and background; comparison of hypotheses; unfolding of experimental distributions; upper/lower bounds in frontier-type measurements. Approximate methods for routine use are derived and ar |
id | cern-642515 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2003 |
publisher | World Scientific |
record_format | invenio |
spelling | cern-6425152021-07-30T13:20:10Zdoi:10.1142/5262http://cds.cern.ch/record/642515engD'Agostini, GiulioBayesian Reasoning in Data Analysis: A Critical IntroductionMathematical Physics and MathematicsThis book provides a multi-level introduction to Bayesian reasoning (as opposed to "conventional statistics") and its applications to data analysis. The basic ideas of this "new" approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference; combination of results; treatment of uncertainty due to systematic errors and background; comparison of hypotheses; unfolding of experimental distributions; upper/lower bounds in frontier-type measurements. Approximate methods for routine use are derived and arWorld Scientificoai:cds.cern.ch:6425152003 |
spellingShingle | Mathematical Physics and Mathematics D'Agostini, Giulio Bayesian Reasoning in Data Analysis: A Critical Introduction |
title | Bayesian Reasoning in Data Analysis: A Critical Introduction |
title_full | Bayesian Reasoning in Data Analysis: A Critical Introduction |
title_fullStr | Bayesian Reasoning in Data Analysis: A Critical Introduction |
title_full_unstemmed | Bayesian Reasoning in Data Analysis: A Critical Introduction |
title_short | Bayesian Reasoning in Data Analysis: A Critical Introduction |
title_sort | bayesian reasoning in data analysis: a critical introduction |
topic | Mathematical Physics and Mathematics |
url | https://dx.doi.org/10.1142/5262 http://cds.cern.ch/record/642515 |
work_keys_str_mv | AT dagostinigiulio bayesianreasoningindataanalysisacriticalintroduction |