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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 are shown often to coincide -- under well-defined assumptions! -- with "standard" methods, which can therefore be seen as special cases of the more general Bayesian methods. In dealing with uncertainty in measurements, modern metrological ideas are utilized, including the ISO classification of uncertainty into type A and type B. These are shown to fit well into the Bayesian framework.
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BAYESIAN REASONING IN DATA ANALYSIS: A CRITICAL INTRODUCTION
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Bayesian Reasoning in Data Analysis: A Critical Introduction
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Bayesian Reasoning in Data Analysis: A Critical Introduction
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Zustand: New. A multi-level introduction to Bayesian reasoning. The basic ideas of this approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference combination of results. Bestandsnummer des Verkäufers 909683241
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Bayesian Reasoning in Data Analysis: A Critical Introduction
Verlag:
World Scientific Publishing Company Jun 2003, 2003
ISBN 10: 9812383565
ISBN 13: 9789812383563
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Hardcover
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Buch. Zustand: Neu. Neuware - 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 are shown often to coincide -- under well-defined assumptions! -- with 'standard' methods, which can therefore be seen as special cases of the more general Bayesian methods. In dealing with uncertainty in measurements, modern metrological ideas are utilized, including the ISO classification of uncertainty into type A and type B. These are shown to fit well into the Bayesian framework. Bestandsnummer des Verkäufers 9789812383563
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