LEADER 05410nam 2200613 450 001 9910132177003321 005 20230803203510.0 010 $a1-118-91476-7 010 $a1-118-91475-9 035 $a(CKB)3710000000167682 035 $a(EBL)1729556 035 $a(OCoLC)884587620 035 $a(MiAaPQ)EBC1729556 035 $a(Au-PeEL)EBL1729556 035 $a(CaPaEBR)ebr10891186 035 $a(EXLCZ)993710000000167682 100 $a20140717h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 00$aBayesian networks for probabilistic inference and decision analysis in forensic science /$fFranco Taroni [and four others] 205 $a2nd ed. 210 1$aChichester, England :$cWiley,$d2014. 210 4$dİ2014 215 $a1 online resource (473 p.) 225 1 $aStatistics in Practice 300 $aDescription based upon print version of record. 311 $a0-470-97973-9 320 $aIncludes bibliographical references and indexes. 327 $aCover; Title Page; Copyright; Contents; Foreword; Preface to the second edition; Preface to the first edition; Chapter 1 The logic of decision; 1.1 Uncertainty and probability; 1.1.1 Probability is not about numbers, it is about coherent reasoning under uncertainty; 1.1.2 The first two laws of probability; 1.1.3 Relevance and independence; 1.1.4 The third law of probability; 1.1.5 Extension of the conversation; 1.1.6 Bayes' theorem; 1.1.7 Probability trees; 1.1.8 Likelihood and probability; 1.1.9 The calculus of (probable) truths; 1.2 Reasoning under uncertainty 327 $a1.2.1 The Hound of the Baskervilles1.2.2 Combination of background information and evidence; 1.2.3 The odds form of Bayes' theorem; 1.2.4 Combination of evidence; 1.2.5 Reasoning with total evidence; 1.2.6 Reasoning with uncertain evidence; 1.3 Population proportions, probabilities and induction; 1.3.1 The statistical syllogism; 1.3.2 Expectations and population proportions; 1.3.3 Probabilistic explanations; 1.3.4 Abduction and inference to the best explanation; 1.3.5 Induction the Bayesian way; 1.4 Decision making under uncertainty; 1.4.1 Bookmakers in the Courtrooms?; 1.4.2 Utility theory 327 $a1.4.3 The rule of maximizing expected utility1.4.4 The loss function; 1.4.5 Decision trees; 1.4.6 The expected value of information; 1.5 Further readings; Chapter 2 The logic of Bayesian networks and influence diagrams; 2.1 Reasoning with graphical models; 2.1.1 Beyond detective stories; 2.1.2 Bayesian networks; 2.1.3 A graphical model for relevance; 2.1.4 Conditional independence; 2.1.5 Graphical models for conditional independence: d-separation; 2.1.6 A decision rule for conditional independence; 2.1.7 Networks for evidential reasoning; 2.1.8 The Markov property; 2.1.9 Influence diagrams 327 $a2.1.10 Conditional independence in influence diagrams2.1.11 Relevance and causality; 2.1.12 The Hound of the Baskervilles revisited; 2.2 Reasoning with Bayesian networks and influence diagrams; 2.2.1 Divide and conquer; 2.2.2 From directed to triangulated graphs; 2.2.3 From triangulated graphs to junction trees; 2.2.4 Solving influence diagrams; 2.2.5 Object-oriented Bayesian networks; 2.2.6 Solving object-oriented Bayesian networks; 2.3 Further readings; 2.3.1 General; 2.3.2 Bayesian networks and their predecessors in judicial contexts 327 $aChapter 3 Evaluation of scientific findings in forensic science3.1 Introduction; 3.2 The value of scientific findings; 3.3 Principles of forensic evaluation and relevant propositions; 3.3.1 Source level propositions; 3.3.1.1 Notation; 3.3.1.2 Single stain; 3.3.2 Activity level propositions; 3.3.2.1 Notation and formulaic development; 3.3.3 Crime level propositions; 3.3.3.1 Notation; 3.3.3.2 Association propositions; 3.3.3.3 Intermediate association propositions; 3.4 Pre-assessment of the case; 3.5 Evaluation using graphical models; 3.5.1 Introduction 327 $a3.5.2 General aspects of the construction of Bayesian networks 330 $a ""This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation""Dr. Ian Evett, Principal Forensic Services Ltd, London, UK Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability t 410 0$aStatistics in practice. 606 $aBayesian statistical decision theory$xGraphic methods 606 $aUncertainty (Information theory)$xGraphic methods 606 $aForensic sciences$xGraphic methods 615 0$aBayesian statistical decision theory$xGraphic methods. 615 0$aUncertainty (Information theory)$xGraphic methods. 615 0$aForensic sciences$xGraphic methods. 676 $a363.2501/519542 702 $aTaroni$b Franco 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910132177003321 996 $aBayesian networks for probabilistic inference and decision analysis in forensic science$92034116 997 $aUNINA