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010   $a3-319-41644-8
024 7 $a10.1007/978-3-319-41644-1
035   $a(CKB)3710000000909084
035   $a(DE-He213)978-3-319-41644-1
035   $a(MiAaPQ)EBC4720729
035   $a(PPN)196325617
035   $a(EXLCZ)993710000000909084
100   $a20161018d2016      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aBayesian Inference $eData Evaluation and Decisions /$fby Hanns Ludwig Harney
205   $a2nd ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016.
215   $a1 online resource (XIII, 243 p. 39 illus., 3 illus. in color.) 
300   $aIncludes index.
311   $a3-319-41642-1 
327   $aKnowledge an Logic -- Bayes' Theorem -- Probable and Improbable Data -- Descriptions of Distributions I: Real x -- Description of Distributions II: Natural x -- Form Invariance I -- Examples of Invariant Measures -- A Linear Representation of Form Invariance -- Going Beyond Form Invariance: The Geometric Prior -- Inferring the Mean or Standard Deviation -- Form Invariance II: Natural x -- Item Response Theory -- On the Art of Fitting  -- Problems and Solutions -- Description of Distributions I -- Real x -- Form Invariance I -- Beyond Form Invariance: The Geometric Prior -- Inferring Mean or Standard Deviation -- Form Invariance II: Natural x -- Item Response Theory -- On the Art of Fitting. .
330   $aThis 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.
606   $aPhysics
606   $aStatistics 
606   $aNuclear physics
606   $aProbabilities
606   $aMedical physics
606   $aRadiation
606   $aComputer mathematics
606   $aMathematical Methods in Physics$3https://scigraph.springernature.com/ontologies/product-market-codes/P19013
606   $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020
606   $aParticle and Nuclear Physics$3https://scigraph.springernature.com/ontologies/product-market-codes/P23002
606   $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004
606   $aMedical and Radiation Physics$3https://scigraph.springernature.com/ontologies/product-market-codes/P27060
606   $aComputational Mathematics and Numerical Analysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M1400X
615  0$aPhysics.
615  0$aStatistics .
615  0$aNuclear physics.
615  0$aProbabilities.
615  0$aMedical physics.
615  0$aRadiation.
615  0$aComputer mathematics.
615 14$aMathematical Methods in Physics.
615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
615 24$aParticle and Nuclear Physics.
615 24$aProbability Theory and Stochastic Processes.
615 24$aMedical and Radiation Physics.
615 24$aComputational Mathematics and Numerical Analysis.
676   $a530.15
700   $aHarney$b Hanns Ludwig$4aut$4http://id.loc.gov/vocabulary/relators/aut$047556
906   $aBOOK
912   $a9910136019303321
996   $aBayesian inference$9671725
997   $aUNINA