LEADER 05341nam 2200637Ia 450 001 9911049052503321 005 20200520144314.0 010 $a9786611928216 010 $a9781281928214 010 $a1281928216 010 $a9789812775511 010 $a981277551X 035 $a(CKB)1000000000551326 035 $a(EBL)1223174 035 $a(SSID)ssj0000289711 035 $a(PQKBManifestationID)12067875 035 $a(PQKBTitleCode)TC0000289711 035 $a(PQKBWorkID)10402286 035 $a(PQKB)10162330 035 $a(MiAaPQ)EBC1223174 035 $a(WSP)00005262 035 $a(Perlego)849489 035 $a(EXLCZ)991000000000551326 100 $a20030314d2003 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian reasoning in data analysis $ea critical introduction /$fGiulio D'Agostini 210 $aSingapore ;$aRiver Edge, NJ $cWorld Scientific$dc2003 215 $a1 online resource (351 p.) 300 $aDescription based upon print version of record. 311 08$a9789812383563 311 08$a9812383565 320 $aIncludes bibliographical references (p. 313-323) and index. 327 $aContents; Preface; PART I Critical review and outline of the Bayesian alternative; 1 Uncertainty in physics and the usual methods of handling it; 1.1 Uncertainty in physics; 1.2 True value, error and uncertainty; 1.3 Sources of measurement uncertainty; 1.4 Usual handling of measurement uncertainties; 1.5 Probability of observables versus probability of 'true values'; 1.6 Probability of the causes; 1.7 Unsuitability of frequentistic confidence intervals; 1.8 Misunderstandings caused by the standard paradigm of hypothesis tests; 1.9 Statistical significance versus probability of hypotheses 327 $a2 A probabilistic theory of measurement uncertainty2.1 Where to restart from?; 2.2 Concepts of probability; 2.3 Subjective probability; 2.4 Learning from observations: the 'problem of induction'; 2.5 Beyond Popper's falsification scheme; 2.6 From the probability of the effects to the probability of the causes; 2.7 Bayes' theorem for uncertain quantities: derivation from a physicist's point of view; 2.8 Afraid of 'prejudices'? Logical necessity versus frequent practical irrelevance of the priors; 2.9 Recovering standard methods and short-cuts to Bayesian reasoning 327 $a2.10 Evaluation of measurement uncertainty: general scheme2.10.1 Direct measurement in the absence of systematic errors; 2.10.2 Indirect measurements; 2.10.3 Systematic errors; 2.10.4 Approximate methods; PART 2 A Bayesian primer; 3 Subjective probability and Bayes' theorem; 3.1 What is probability?; 3.2 Subjective definition of probability; 3.3 Rules of probability; 3.4 Subjective probability and 'objective' description of the physical world; 3.5 Conditional probability and Bayes' theorem; 3.5.1 Dependence of the probability on the state of information; 3.5.2 Conditional probability 327 $a3.5.3 Bayes' theorem3.5.4 'Conventional' use of Bayes' theorem; 3.6 Bayesian statistics: learning by experience; 3.7 Hypothesis 'test' (discrete case); 3.7.1 Variations over a problem to Newton; 3.8 Falsificationism and Bayesian statistics; 3.9 Probability versus decision; 3.10 Probability of hypotheses versus probability of observations; 3.11 Choice of the initial probabilities (discrete case); 3.11.1 General criteria; 3.11.2 Insufficient reason and Maximum Entropy; 3.12 Solution to some problems; 3.12.1 AIDS test; 3.12.2 Gold/silver ring problem; 3.12.3 Regular or double-head coin? 327 $a3.12.4 Which random generator is responsible for the observed number?3.13 Some further examples showing the crucial role of background knowledge; 4 Probability distributions (a concise reminder); 4.1 Discrete variables; 4.2 Continuous variables: probability and probability density function; 4.3 Distribution of several random variables; 4.4 Propagation of uncertainty; 4.5 Central limit theorem; 4.5.1 Terms and role; 4.5.2 Distribution of a sample average; 4.5.3 Normal approximation of the binomial and of the Poisson distribution; 4.5.4 Normal distribution of measurement errors; 4.5.5 Caution 327 $a4.6 Laws of large numbers 330 $aThis book provides a multi-level introduction to Bayesian reasoning (as opposed to "conventional statistics") and its applications to data analysis. The basic ideas of this "new" approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference; combination of results; treatment of uncertainty due to systematic errors and background; comparison of hypotheses; unfolding of experimental distributions; upper/lower bounds in frontier-type measurements. Approximate methods for routine use are derived and ar 606 $aBayesian statistical decision theory 606 $aStatistical decision 615 0$aBayesian statistical decision theory. 615 0$aStatistical decision. 676 $a519.5/42 700 $aD'Agostini$b G$g(Giulio)$01888085 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911049052503321 996 $aBayesian reasoning in data analysis$94526411 997 $aUNINA