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Bayesian reasoning in data analysis ...

D'Agostini, G.

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Bayesian reasoning in data analysis : = a critical introduction /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Bayesian reasoning in data analysis :/ Giulio D'Agostini.
其他題名:	a critical introduction /
作者:	D'Agostini, G.
出版者:	Singapore ;World Scientific, : 2003.,
面頁冊數:	xix, 329 p. :ill. ;24 cm.
內容註:	Pt. 1. Critical review and outline of the Bayesian alternative -- 1. Uncertainty in physics and the usual methods of handling it -- 2. A probabilistic theory of measurement uncertainty -- Pt. 2. A Bayesian primer -- 3. Subjective probability and Bayes' theorem -- 4. Probability distributions (a concise reminder) -- 5. Bayesian inference of continuous quantities -- 6. Gaussian likelihood -- 7. Counting experiments -- 8. Bypassing Bayes' theorem for routine applications -- 9. Bayesian unfolding -- Pt. 3. Further comments, examples and applications -- 10. Miscellanea on general issues in probability and inference -- 11. Combination of experimental results: a closer look -- 12. Asymmetric uncertainties and nonlinear propagation -- 13. Which priors for frontier physics? -- Pt. 4. Conclusion -- 14. Conclusions and bibliography.
標題:	Bayesian statistical decision theory. -
ISBN:	9789812383563

Bayesian reasoning in data analysis : = a critical introduction /
D'Agostini, G.

Bayesian reasoning in data analysis :a critical introduction /Giulio D'Agostini. - Singapore ;World Scientific,2003. - xix, 329 p. :ill. ;24 cm.

Includes bibliographical references (p. 313-323) and index.

Pt. 1. Critical review and outline of the Bayesian alternative -- 1. Uncertainty in physics and the usual methods of handling it -- 2. A probabilistic theory of measurement uncertainty -- Pt. 2. A Bayesian primer -- 3. Subjective probability and Bayes' theorem -- 4. Probability distributions (a concise reminder) -- 5. Bayesian inference of continuous quantities -- 6. Gaussian likelihood -- 7. Counting experiments -- 8. Bypassing Bayes' theorem for routine applications -- 9. Bayesian unfolding -- Pt. 3. Further comments, examples and applications -- 10. Miscellanea on general issues in probability and inference -- 11. Combination of experimental results: a closer look -- 12. Asymmetric uncertainties and nonlinear propagation -- 13. Which priors for frontier physics? -- Pt. 4. Conclusion -- 14. Conclusions and bibliography.

AnnotationThis 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.

ISBN: 9789812383563US145.00

LCCN: 2003045082Subjects--Topical Terms:

551404
Bayesian statistical decision theory.

LC Class. No.: QA279.5 / .D28 2003

Dewey Class. No.: 519.5/42

Bayesian reasoning in data analysis : = a critical introduction /
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260 # $a Singapore ; $a Hackensack, NJ : $b World Scientific, $c 2003.
300 $a xix, 329 p. : $b ill. ; $c 24 cm.
504 $a Includes bibliographical references (p. 313-323) and index.
505 0 # $a Pt. 1. Critical review and outline of the Bayesian alternative -- 1. Uncertainty in physics and the usual methods of handling it -- 2. A probabilistic theory of measurement uncertainty -- Pt. 2. A Bayesian primer -- 3. Subjective probability and Bayes' theorem -- 4. Probability distributions (a concise reminder) -- 5. Bayesian inference of continuous quantities -- 6. Gaussian likelihood -- 7. Counting experiments -- 8. Bypassing Bayes' theorem for routine applications -- 9. Bayesian unfolding -- Pt. 3. Further comments, examples and applications -- 10. Miscellanea on general issues in probability and inference -- 11. Combination of experimental results: a closer look -- 12. Asymmetric uncertainties and nonlinear propagation -- 13. Which priors for frontier physics? -- Pt. 4. Conclusion -- 14. Conclusions and bibliography.
520 8 $a Annotation $b 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.
650 # 0 $a Bayesian statistical decision theory. $3 551404