Bayesian Inference: Parameter Estimation and Decisions

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 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.