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Books > Science & Mathematics > Physics > Particle & high-energy physics
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Bayesian Inference - Data Evaluation and Decisions (Paperback, Softcover reprint of the original 2nd ed. 2016)
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Bayesian Inference - Data Evaluation and Decisions (Paperback, Softcover reprint of the original 2nd ed. 2016)
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This new edition offers a comprehensive introduction to the
analysis of data using Bayes rule. It generalizes Gaussian error
intervals to situations in which the data follow distributions
other than Gaussian. This is particularly useful when the observed
parameter is barely above the background or the histogram of
multiparametric data contains many empty bins, so that 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. New sections feature factorizing parameters,
commuting parameters, observables in quantum mechanics, the art of
fitting with coherent and with incoherent alternatives and fitting
with multinomial distribution. Additional problems and examples
help deepen the knowledge. Requiring no knowledge of quantum
mechanics, the book is written on introductory level, with many
examples and exercises, for advanced undergraduate and graduate
students in the physical sciences, planning to, or working in,
fields such as medical physics, nuclear physics, quantum mechanics,
and chaos.
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