ISBN: 9789812383563 - bayesian reasoning in data analysis: a critical introduction (4 risultati)

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Da: HPB-Red, Dallas, TX, U.S.A.HPB-Red
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Buono
EUR 116,47
EUR 3,38 spedizioneSpedito in U.S.A.Quantità: 1 disponibile
hardcover. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 152,38
EUR 14,96 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 2 disponibili
Hardcover. Condizione: Brand New. 329 pages. 9.00x6.00x0.75 inches. In Stock.

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Da: moluna, Greven, Germaniamoluna
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 152,07
EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Condizione: 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.

Lingua: Inglese
Editore: World Scientific Publishing Company Jun 2003, 2003
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 203,69
EUR 35,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Buch. Condizione: 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.…