Bayesian Inference: Data Evaluation and Decisions
Author:   Hanns Ludwig Harney
Publisher:   Springer International Publishing AG
Edition:   2nd ed. 2016
ISBN:  

9783319416427


Pages:   243
Publication Date:   26 October 2016
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Bayesian Inference: Data Evaluation and Decisions


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Author:   Hanns Ludwig Harney
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2nd ed. 2016
Dimensions:   Width: 15.50cm , Height: 1.60cm , Length: 23.50cm
Weight:   5.148kg
ISBN:  

9783319416427


ISBN 10:   3319416421
Pages:   243
Publication Date:   26 October 2016
Audience:   College/higher education ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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. 

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Author Information

Hanns Ludwig Harney, born in 1939, professor at the University of Heidelberg. He has contributed to experimental and theoretical physics within the Max-Planck Institute for Nuclear Physics at Heidelberg. His interest is focused on symmetries, such as isospin and its violation, as well as chaos, observed as reproducible fluctuations. Since the 1990's, the symmetry properties of common probability distributions lead him to a reformulation of Bayesian inference.

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