LEADER 05320nam 2200625Ia 450 001 9910814422903321 005 20200520144314.0 010 $a1-282-54840-9 010 $a9786612548406 010 $a0-470-66508-4 010 $a0-470-66507-6 035 $a(CKB)2670000000014732 035 $a(EBL)514398 035 $a(OCoLC)609862743 035 $a(SSID)ssj0000358663 035 $a(PQKBManifestationID)11257130 035 $a(PQKBTitleCode)TC0000358663 035 $a(PQKBWorkID)10380479 035 $a(PQKB)10877076 035 $a(MiAaPQ)EBC514398 035 $a(EXLCZ)992670000000014732 100 $a20100121d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aData analysis in forensic science $ea Bayesian decision perspective /$fFranco Taroni ... [et al.] 210 $aChichester $cJohn Wiley and Sons$d2010 215 $a1 online resource (389 p.) 225 1 $aStatistics in practice 300 $aDescription based upon print version of record. 311 $a0-470-99835-0 320 $aIncludes bibliographical references and index. 327 $aData Analysis in Forensic Science; Contents; Foreword; Preface; I The Foundations of Inference and Decision in Forensic Science; 1 Introduction; 1.1 The Inevitability of Uncertainty; 1.2 Desiderata in Evidential Assessment; 1.3 The Importance of the Propositional Framework and the Nature of Evidential Assessment; 1.4 From Desiderata to Applications; 1.5 The Bayesian Core of Forensic Science; 1.6 Structure of the Book; 2 Scientific Reasoning and Decision Making; 2.1 Coherent Reasoning Under Uncertainty; 2.1.1 A rational betting policy; 2.1.2 A rational policy for combining degrees of belief 327 $a2.1.3 A rational policy for changing degrees of belief2.2 Coherent Decision Making Under Uncertainty; 2.2.1 A method for measuring the value of consequences; 2.2.2 The consequences of rational preferences; 2.2.3 Intermezzo: some more thoughts about rational preferences; 2.2.4 The implementation of coherent decision making under uncertainty: Bayesian networks; 2.2.5 The connection between pragmatic and epistemic standards of reasoning; 2.3 Scientific Reasoning as Coherent Decision Making; 2.3.1 Bayes' theorem; 2.3.2 The theories' race; 2.3.3 Statistical reasoning: the models' race 327 $a2.3.4 Probabilistic model building: betting on random quantities2.4 Forensic Reasoning as Coherent Decision Making; 2.4.1 Likelihood ratios and the 'weight of evidence'; 2.4.2 The expected value of information; 2.4.3 The hypotheses' race in the law; 3 Concepts of Statistical Science and Decision Theory; 3.1 Random Variables and Distribution Functions; 3.1.1 Univariate random variables; 3.1.2 Measures of location and variability; 3.1.3 Multiple random variables; 3.2 Statistical Inference and Decision Theory; 3.2.1 Utility theory; 3.2.2 Maximizing expected utility; 3.2.3 The loss function 327 $a3.3 The Bayesian Paradigm3.3.1 Sequential use of Bayes' theorem; 3.3.2 Principles of rational inference in statistics; 3.3.3 Prior distributions; 3.3.4 Predictive distributions; 3.3.5 Markov Chain Monte Carlo methods (MCMC); 3.4 Bayesian Decision Theory; 3.4.1 Optimal decisions; 3.4.2 Standard loss functions; 3.5 R Code; II Forensic Data Analysis; 4 Point Estimation; 4.1 Introduction; 4.2 Bayesian Decision for a Proportion; 4.2.1 Estimation when there are zero occurrences in a sample; 4.2.2 Prior probabilities; 4.2.3 Prediction 327 $a4.2.4 Inference for 0 in the presence of background data on the number of successes4.2.5 Multinomial variables; 4.3 Bayesian Decision for a Poisson Mean; 4.3.1 Inference about the Poisson parameter in the absence of background events; 4.3.2 Inference about the Poisson parameter in the presence of background events; 4.3.3 Forensic inference using graphical models; 4.4 Bayesian Decision for Normal Mean; 4.4.1 Case with known variance; 4.4.2 Case with unknown variance; 4.4.3 Estimation of the mean in the presence of background data; 4.5 R Code; 5 Credible Intervals; 5.1 Introduction 327 $a5.2 Credible Intervals and Lower Bounds 330 $aThis is the first text to examine the use of statistical methods in forensic science and bayesian statistics in combination. The book is split into two parts: Part One concentrates on the philosophies of statistical inference. Chapter One examines the differences between the frequentist, the likelihood and the Bayesian perspectives, before Chapter Two explores the Bayesian decision-theoretic perspective further, and looks at the benefits it carries. Part Two then introduces the reader to the practical aspects involved: the application, interpretation, summary and presentation of data analy 410 0$aStatistics in practice. 606 $aForensic sciences$xStatistical methods 606 $aBayesian statistical decision theory 615 0$aForensic sciences$xStatistical methods. 615 0$aBayesian statistical decision theory. 676 $a363.2501/519542 701 $aTaroni$b Franco$01709604 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910814422903321 996 $aData analysis in forensic science$94099466 997 $aUNINA