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Bayesian Inference
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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. The text is written at introductory level, with many examples and exercises.The book provides a generalization of Gaussian error intervals tosituations where the data follow non-Gaussian distributions. Thisusually occurs in frontier science, where the observed parameter isjust above background or the histogram of multiparametric datacontains empty bins. Then the validity of a theorycannot be decided by the chi-squared-criterion, but this long-standingproblem is solved here. The book is based on Bayes' theorem, symmetry anddifferential geometry. In addition to solutions of practical problems, the textprovides an epistemic insight: The logic of quantum mechanics isobtained 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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