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Book Description
Filling a longstanding need in the physical sciences, Bayesian Inference offers the first basic introduction for advanced undergraduates and graduates 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. In this case, the determination of the validity of a theory cannot be based on the chi-squared-criterion. In addition to the solutions of practical problems, this approach provides an epistemic 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, for physicists planning to, or working in, fields such as medical physics, nuclear physics, quantum mechanics, and chaos.
Book Info
Text provides a generalization of Gaussian error intervals to situations where the data follow non-Gaussian distributions; based on Bayes' theorem, symmetry, and differential geometry. Includes numerous examples and illustrations taken from recent research. DLC: Bayesian statistical decision theory.
Bayesian Inference,Hanns L. Harney,H.L. Harney,Springer,3540003975,Bayesian statistical decision,Bayesian statistical decision theory,Mathematical Physics,Physics,Probability & Statistics - Bayesian Analysis,Quantum Theory,Science,Science/Mathematics,Bayes Theorem,Data Analysis,Econophysics,Fitting Data,Invariance,Invariant Meassure,Non Gaussian Distribution,Science / Physics
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