Bayesian Networks and Probabilistic Inference in Forensic Science

Franco Taroni - University of Lausanne, Switzerland Colin Aitken - University of Edinburgh, UK Paolo Garbolino - University IUAV of Venice, Italy Alex Biedermann - University of Lausanne and Federal Office of Police, Berne, Switzerland

Preface. Foreword. 1. The logic of uncertainty. 1.1 Uncertainty and probability. 1.2 Reasoning under uncertainty. 1.3 Frequencies and probabilities. 1.4 Induction and probability. 1.5 Further readings. 2. The logic of Bayesian networks. 2.1 Reasoning with graphical models. 2.2 Reasoning with Bayesian networks. 2.3 Further readings. 3. Evaluation of scientific evidence. 3.1 Introduction. 3.2 The value of evidence. 3.3 Relevant propositions. 3.4 Pre-assessment of the case. 3.5 Evaluation using graphical models. 4. Bayesian networks for evaluating scientific evidence. 4.1 Issues in one-trace transfer cases. 4.2 When evidence has more than one component: footwear marks evidence. 4.3 Scenarios with more than one stain. 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. 5.9 Relatedness testing. 5.10 Database search. 5.11 Error rates. 5.12 Sub-population and co-ancestry coefficient. 5.13 Further reading. 6. Transfer evidence. 6.1 Assessment of transfer evidence under crime level propositions. 6.2 Assessment of transfer evidence under activity level propositions. 6.3 Cross- or two-way transfer of evidential material. 6.4 Increasing the level of detail of selected nodes. 6.5 Missing evidence. 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. 7.4 Combination of distinct items of evidence. 8. Pre-assessment. 8.1 Introduction. 8.2 Pre-assessment. 8.3 Pre-assessment for a fibres scenario. 8.4 Pre-assessment in a cross-transfer scenario. 8.5 Pre-assessment with multiple propositions. 8.6 Remarks. 9. Qualitative and sensitivity analyses. 9.1 Qualitative probability models. 9.2 Sensitivity analyses. 10. Continuous networks. 10.1 Introduction. 10.2 Samples and estimates. 10.3 Measurements. 10.4 Use of a continuous distribution which is not normal. 10.5 Appendix. 11. Further applications. 11.1 Offender profiling. 11.2 Decision making. Bibliography. Author Index. Subject Index.