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Bayesian inference[electronic resource] :data evaluation and decisions /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	519.542
書名/作者:	Bayesian inference : data evaluation and decisions // by Hanns Ludwig Harney.
作者:	Harney, Hanns Ludwig.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xiii, 243 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Mathematical physics.
標題:	Physics.
標題:	Mathematical Methods in Physics.
標題:	Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
標題:	Particle and Nuclear Physics.
標題:	Probability Theory and Stochastic Processes.
標題:	Medical and Radiation Physics.
標題:	Bayesian statistical decision theory.
標題:	Computational Mathematics and Numerical Analysis.
ISBN:	9783319416441
ISBN:	9783319416427
內容註:	Knowledge an Logic -- Bayes' Theorem -- Probable and Improbable Data -- Descriptions of Distributions I: Real x -- Description of Distributions II: Natural x -- Form Invariance I -- Examples of Invariant Measures -- A Linear Representation of Form Invariance -- Going Beyond Form Invariance: The Geometric Prior -- Inferring the Mean or Standard Deviation -- Form Invariance II: Natural x -- Item Response Theory -- On the Art of Fitting -- Problems and Solutions -- Description of Distributions I -- Real x -- Form Invariance I -- Beyond Form Invariance: The Geometric Prior -- Inferring Mean or Standard Deviation -- Form Invariance II: Natural x -- Item Response Theory -- On the Art of Fitting.
摘要、提要註:	This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This is particularly useful when the observed parameter is barely above the background or the histogram of multiparametric data contains many empty bins, so that the determination of the validity of a theory cannot be based on the chi-squared-criterion. In addition to the solutions of practical problems, this approach provides an epistemic insight: the logic of quantum mechanics is obtained as the logic of unbiased inference from counting data. New sections feature factorizing parameters, commuting parameters, observables in quantum mechanics, the art of fitting with coherent and with incoherent alternatives and fitting with multinomial distribution. Additional problems and examples help deepen the knowledge. Requiring no knowledge of quantum mechanics, the book is written on introductory level, with many examples and exercises, for advanced undergraduate and graduate students in the physical sciences, planning to, or working in, fields such as medical physics, nuclear physics, quantum mechanics, and chaos.
電子資源:	http://dx.doi.org/10.1007/978-3-319-41644-1

Bayesian inference[electronic resource] :data evaluation and decisions /
Harney, Hanns Ludwig.

Bayesian inferencedata evaluation and decisions /[electronic resource] :by Hanns Ludwig Harney. - 2nd ed. - Cham :Springer International Publishing :2016. - xiii, 243 p. :ill., digital ;24 cm.

Knowledge an Logic -- Bayes' Theorem -- Probable and Improbable Data -- Descriptions of Distributions I: Real x -- Description of Distributions II: Natural x -- Form Invariance I -- Examples of Invariant Measures -- A Linear Representation of Form Invariance -- Going Beyond Form Invariance: The Geometric Prior -- Inferring the Mean or Standard Deviation -- Form Invariance II: Natural x -- Item Response Theory -- On the Art of Fitting -- Problems and Solutions -- Description of Distributions I -- Real x -- Form Invariance I -- Beyond Form Invariance: The Geometric Prior -- Inferring Mean or Standard Deviation -- Form Invariance II: Natural x -- Item Response Theory -- On the Art of Fitting.

This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This is particularly useful when the observed parameter is barely above the background or the histogram of multiparametric data contains many empty bins, so that the determination of the validity of a theory cannot be based on the chi-squared-criterion. In addition to the solutions of practical problems, this approach provides an epistemic insight: the logic of quantum mechanics is obtained as the logic of unbiased inference from counting data. New sections feature factorizing parameters, commuting parameters, observables in quantum mechanics, the art of fitting with coherent and with incoherent alternatives and fitting with multinomial distribution. Additional problems and examples help deepen the knowledge. Requiring no knowledge of quantum mechanics, the book is written on introductory level, with many examples and exercises, for advanced undergraduate and graduate students in the physical sciences, planning to, or working in, fields such as medical physics, nuclear physics, quantum mechanics, and chaos.

ISBN: 9783319416441

Standard No.: 10.1007/978-3-319-41644-1doiSubjects--Topical Terms:

182314
Mathematical physics.

LC Class. No.: QA279.5

Dewey Class. No.: 519.542

Bayesian inference[electronic resource] :data evaluation and decisions /
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