Bayesian Inference (eBook, PDF)
Parameter Estimation and Decisions
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Solving a longstanding problem in the physical sciences, this text and reference generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This usually occurs in frontier science because the observed parameter is barely above the background or the histogram of multiparametric data contains many empty bins. Then the determination of the validity of a theory cannot be based on the chi-square-criterion. The book is based on Bayes' theorem, symmetries and differential geometry. In addition to the solutions of practical problems, this approach pr...
Solving a longstanding problem in the physical sciences, this text and reference generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This usually occurs in frontier science because the observed parameter is barely above the background or the histogram of multiparametric data contains many empty bins. Then the determination of the validity of a theory cannot be based on the chi-square-criterion. The book is based on Bayes' theorem, symmetries and differential geometry. In addition to the solutions of practical problems, this approach provides an espithemic insight: the logic of quantum mechanics is obtained as the logic of unbiased inference from counting data. Requiring no knowledge of quantum mechanics. The text is written on introductory level, with many examples and exercises.
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