Centro de Documentação da PJ
Monografia
 30827 | ((AFD)) |  |
Bayesian networks and probabilistic inference in forensic science / Franco Taroni .. [et al.].- West Sussex : John Wiley & Sons, 2006. - (Statistics in practice)
ISBN 978-0-470-09173-9; ISBN-10: 0-470-09173-8
CIÊNCIA FORENSE, ESTATÍSTICA, MÉTODO ANALÍTICO, ESTUDO DE CASOS
Preface. Foreword. 1 The logic of uncertainty. 1.1 Uncertainty and probability. 1.1.1 Probability is not about numbers. 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 Another look at probability updating. 1.1.8 Likelihood and probability. 1.1.9 The calculus of (probable) truths. 1.2 Reasoning under uncertainty. 1.2.1 The Hound of the Baskervilles. 1.2.2 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 Frequencies and probabilities. 1.3.1 The statistical syllogism. 1.3.2 Expectations and frequencies. 1.3.3 Bookmakers in the Courtrooms? 1.4 Induction and probability. 1.4.1 Probabilistic explanations. 1.4.2 Abduction and inference to the best explanation. 1.4.3 Induction the Bayesian way. 1.5 Further readings. 2 The logic of Bayesian networks. 2.1 Reasoning with graphical models. 2.1.1 Beyond detective stories. 2.1.2 What Bayesian networks are and what they can do. 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 Relevance and causality. 2.1.9 The Hound of the Baskervilles revisited. 2.2 Reasoning with Bayesian networks. 2.2.1 ‘Jack loved Lulu’ . 2.2.2 TheMarkov property. 2.2.3 Divide and conquer. 2.2.4 From directed to triangulated graphs. 2.2.5 From triangulated graphs to junction trees. 2.2.6 Calculemus. 2.2.7 A probabilistic machine. 2.3 Further readings. 2.3.1 General. 2.3.2 Bayesian networks in judicial contexts. 3 Evaluation of scientific evidence. 3.1 Introduction. 3.2 The value of evidence. 3.3 Relevant propositions. 3.3.1 Source level. 3.3.2 Activity level. 3.3.3 Crime level. 3.4 Pre-assessment of the case. 3.5 Evaluation using graphical models. 3.5.1 Introduction. 3.5.2 Aspects of constructing Bayesian networks. 3.5.3 Eliciting structural relationships. 3.5.4 Level of detail of variables and quantification of influences. 3.5.5 Derivation of an alternative network structure. 4 Bayesian networks for evaluating scientific evidence. 4.1 Issues in one-trace transfer cases. 4.1.1 Evaluation of the network. 4.2 When evidence has more than one component: footwear marks evidence. 4.2.1 General considerations. 4.2.2 Addition of further propositions. 4.2.3 Derivation of the likelihood ratio. 4.2.4 Consideration of distinct components. 4.2.5 A practical example. 4.2.6 An extension to firearm evidence. 4.2.7 A note on the evaluation of the likelihood ratio. 4.3 Scenarios with more than one stain. 4.3.1 Two stains, one offender. 4.3.2 Two stains, no putative source. 5 DNA evidence. 5.1 DNA likelihood ratio. 5.2 Network approaches to the DNA likelihood ratio. 5.3 Missing suspect. 5.4 Analysis when the alternative proposition is that a sibling of the suspect left the stain. 5.5 Interpretation with more than two propositions. 5.6 Evaluation of evidence with more than two propositions. 5.7 Partial matches. 5.8 Mixtures. 5.8.1 A three-allele mixture scenario. 5.8.2 A Bayesian network. 5.9 Relatedness testing. 5.9.1 A disputed paternity. 5.9.2 An extended paternity scenario. 5.9.3 Y-chromosomal analysis. 5.10 Database search. 5.10.1 A probabilistic solution to a database search scenario. 5.10.2 A Bayesian network for a database search scenario. 5.11 Error rates. 5.11.1 A probabilistic approach to error rates. 5.11.2 A Bayesian network for error rates. 5.12 Sub-population and co-ancestry coefficient. 5.12.1 Hardy-Weinberg equilibrium. 5.12.2 Variation in sub-population allele frequencies. 5.12.3 A graphical structure for FST. 5.12.4 DNA likelihood ratio. 5.13 Further reading. 6 Transfer evidence. 6.1 Assessment of transfer evidence under crime level propositions. 6.1.1 A single-offender scenario. 6.1.2 A fibre scenario with multiple offenders. 6.2 Assessment of transfer evidence under activity level propositions. 6.2.1 Preliminaries. 6.2.2 Derivation of a basic structure for a Bayesian network. 6.2.3 Stain found on a suspect’s clothing. 6.2.4 Fibres found on a car seat. 6.2.5 The Background node. 6.2.6 Background from different sources. 6.2.7 A note on the Match node. 6.2.8 A match considered in terms of components y and x. 6.2.9 A structure for a Bayesian network. 6.2.10 Evaluation of the proposed model. 6.3 Cross- or two-way transfer of evidential material. 6.4 Increasing the level of detail of selected nodes. 6.5 Missing evidence. 6.5.1 Determination of a structure for a Bayesian network. 7 Aspects of the combination of evidence. 7.1 Introduction. 7.2 A difficulty in combining evidence. 7.3 The likelihood ratio and the combination of evidence. 7.3.1 Conditionally independent items of evidence. 7.3.2 Conditionally non-independent items of evidence. 7.4 Combination of distinct items of evidence. 7.4.1 Example 1: Handwriting and fingermarks evidence. 7.4.2 Example 2: Issues in DNA analysis. 7.4.3 Example 3: Scenario with one offender and two corresponding items of evidence. 7.4.4 Example 4: Scenarios involving firearms. 8 Pre-assessment. 8.1 Introduction. 8.2 Pre-assessment. 8.3 Pre-assessment for a fibres scenario. 8.3.1 Preliminaries. 8.3.2 Propositions and relevant events. 8.3.3 Expected likelihood ratios. 8.3.4 Construction of a Bayesian network. 8.4 Pre-assessment in a cross-transfer scenario. 8.4.1 Preliminaries. 8.4.2 A Bayesian network for a pre-assessment of a cross-transfer scenario. 8.4.3 The expected weight of evidence. 8.5 Pre-assessment with multiple propositions. 8.5.1 Preliminaries. 8.5.2 Construction of a Bayesian network. 8.5.3 Evaluation of different scenarios. 8.5.4 An alternative graphical structure. 8.6 Remarks. 9 Qualitative and sensitivity analyses. 9.1 Qualitative probability models. 9.1.1 Qualitative influence. 9.1.2 Additive synergy. 9.1.3 Product synergy. 9.1.4 Properties of qualitative relationships. 9.1.5 Evaluation of indirect influences between separated nodes: a forensic example. 9.1.6 Implications of qualitative graphical models. 9.2 Sensitivity analyses. 9.2.1 Sensitivity to a single parameter. 9.2.2 One-way sensitivity analysis based on a likelihood ratio. 9.2.3 A further example of one-way sensitivity analysis. 9.2.4 Sensitivity to two parameters. 9.2.5 Further issues in sensitivity analyses. 10 Continuous networks. 10.1 Introduction. 10.2 Samples and estimates. 10.3 Measurements. 10.3.1 Summary statistics. 10.3.2 Normal distribution. 10.3.3 Propagation in a continuous Bayesian network. 10.3.4 Propagation in mixed networks. 10.3.5 Example of mixed network. 10.4 Use of a continuous distribution which is not normal. 10.5 Appendix. 10.5.1 Conditional expectation and variance. 10.5.2 Bayesian network for three serially connected continuous variables. 10.5.3 Bayesian network for a continuous variable with a binary parent. 10.5.4 Bayesian network for a continuous variable with a continuous parent and a binary parent, unmarried. 11 Further applications. 11.1 Offender profiling. 11.2 Decision making. 11.2.1 Decision analysis. 11.2.2 Bayesian networks and decision networks. 11.2.3 Forensic decision analyses. Bibliography.