Bayesian Inference
Parameter Estimation and Decisions
by Hanns L. Harney
The book provides a generalization of Gaussian error intervals to situations where the data follow non-Gaussian distributions. This usually occurs in frontier science, where the observed parameter is just above background or the histogram of multiparametric data contains empty bins. Then the validity of a theory cannot be decided by the chi-squared-criterion, but this long-standing problem is solved here. The book is based on Bayes' theorem, symmetry and differential geometry. In addition to solutions of practical problems, the text provides an epistemic insight: The logic of quantum mechanics is obtained as the logic of unbiased inference from counting data. However, no knowledge of quantum mechanics is required. The text, examples and exercises are written at an introductory level.
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Bayesian Inference: Parameter Estimation and Decisions
2003, Springer Berlin Heidelberg
electronic resource :
in English
364205577X 9783642055775
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