Bayesian Inference

Beschreibung

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.

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.

Autor*in

Hanns L. Harney

Themen in »Bayesian Inference«

Bayes Theorem Data Analysis Econophysics Fitting Fitting Data Invariance Invariant Meassure Non Gaussian Distribution Variance best fit correlation quantum mechanics standard deviation

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