Bayesian networks and probabilistic inference in forensic science:
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Chichester [u.a.]
Wiley
2006
|
| Series: | Wiley series on statistics in practice
|
| Subjects: | |
| Online Access: | kostenfrei Inhaltsverzeichnis Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and indexes The logic of uncertainty -- The logic of Bayesian networks -- Evaluation of scientific evidence -- Bayesian networks for evaluating scientific evidence -- DNA evidence -- Transfer evidence -- Aspects of the combination of evidence -- Pre-assessment -- Qualitative and sensitivity analyses -- Continuous networks -- Further applications |
| Physical Description: | XVIII, 354 S. graph. Darst. |
| ISBN: | 0470091738 9780470091739 |
Staff View
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| adam_text | Contents
Preface xiii
Foreword xvii
1 The logic of uncertainty 1
1.1 Uncertainty and probability 1
1.1.1 Probability is not about numbers 1
1.1.2 The first two laws of probability 2
1.1.3 Relevance and independence 3
1.1.4 The third law of probability 4
1.1.5 Extension of the conversation 5
1.1.6 Bayes theorem 6
1.1.7 Another look at probability updating 7
1.1.8 Likelihood and probability 9
1.1.9 The calculus of (probable) truths 10
1.2 Reasoning under uncertainty 12
1.2.1 The Hound of the Baskervilles 12
1.2.2 Combination of background information and evidence 14
1.2.3 The odds form of Bayes theorem 16
1.2.4 Combination of evidence 16
1.2.5 Reasoning with total evidence 17
1.2.6 Reasoning with uncertain evidence 19
1.3 Frequencies and probabilities 20
1.3.1 The statistical syllogism 20
1.3.2 Expectations and frequencies 22
1.3.3 Bookmakers in the Courtrooms? 23
1.4 Induction and probability 23
1.4.1 Probabilistic explanations 23
1.4.2 Abduction and inference to the best explanation 27
1.4.3 Induction the Bayesian way 28
1.5 Further readings 30
2 The logic of Bayesian networks 33
2.1 Reasoning with graphical models 33
2.1.1 Beyond detective stories 33
viii CONTENTS
2.1.2 What Bayesian networks are and what they can do 34
2.1.3 A graphical model for relevance 37
2.1.4 Conditional independence 38
2.1.5 Graphical models for conditional independence: d-separation .... 40
2.1.6 A decision rule for conditional independence 42
2.1.7 Networks for evidential reasoning 43
2.1.8 Relevance and causality 45
2.1.9 The Hound of the Baskervilles revisited 47
2.2 Reasoning with Bayesian networks 50
2.2.1 Jack loved Lulu 50
2.2.2 The Markov property 51
2.2.3 Divide and conquer 54
2.2.4 From directed to triangulated graphs 55
2.2.5 From triangulated graphs to junction trees 58
2.2.6 Calculemm 60
2.2.7 A probabilistic machine 63
2.3 Further readings 66
2.3.1 General 66
2.3.2 Bayesian networks in judicial contexts 68
3 Evaluation of scientific evidence 69
3.1 Introduction 69
3.2 The value of evidence 70
3.3 Relevant propositions 73
3.3.1 Source level 74
3.3.2 Activity level 77
3.3.3 Crime level 80
3.4 Pre-assessment of the case 84
3.5 Evaluation using graphical models 87
3.5.1 Introduction 87
3.5.2 Aspects of constructing Bayesian networks 87
3.5.3 Eliciting structural relationships 88
3.5.4 Level of detail of variables and quantification of influences 89
3.5.5 Derivation of an alternative network structure 92
4 Bayesian networks for evaluating scientific evidence 97
4.1 Issues in one-trace transfer cases 97
4.1.1 Evaluation of the network 99
4.2 When evidence has more than one component: footwear marks evidence . . 101
4.2.1 General considerations 102
4.2.2 Addition of further propositions 102
4.2.3 Derivation of the likelihood ratio 103
4.2-4 Consideration of distinct components 106
4.2.5 A practical example . 107
4.2.6 An extension to firearm evidence 110
4.2.7 A note on the evaluation of the likelihood ratio 116
CONTENTS ix
4.3 Scenarios with more than one stain 117
4.3.1 Two stains, one offender 117
4.3.2 Two stains, no putative source 123
5 DNA evidence 131
5.1 DNA likelihood ratio 131
5.2 Network approaches to the DNA likelihood ratio 133
5.3 Missing suspect 136
5.4 Analysis when the alternative proposition is that a sibling of the suspect left
the stain 139
5.5 Interpretation with more than two propositions 145
5.6 Evaluation of evidence with more than two propositions 150
5.7 Partial matches 152
5.8 Mixtures 155
5.8.1 A three-allele mixture scenario 156
5.8.2 A Bayesian network 157
5.9 Relatedness testing 160
5.9.1 A disputed paternity 160
5.9.2 An extended paternity scenario 163
5.9.3 Y-chromosomal analysis 164
5.10 Database search 165
5.10.1 A probabilistic solution to a database search scenario 166
5.10.2 A Bayesian network for a database search scenario 167
5.11 Error rates 168
5.11.1 A probabilistic approach to error rates 169
5.11.2 A Bayesian network for error rates 170
5.12 Sub-population and co-ancestry coefficient 172
5.12.1 Hardy-Weinberg equilibrium 172
5.12.2 Variation in sub-population allele frequencies 173
5.12.3 A graphical structure for F $t 174
5.12.4 DNA likelihood ratio 175
5.13 Further reading 180
6 Transfer evidence 183
6.1 Assessment of transfer evidence under crime level propositions 184
6.1.1 A single-offender scenario 184
6.1.2 A fibre scenario with multiple offenders 186
6.2 Assessment of transfer evidence under activity level propositions 188
6.2.1 Preliminaries 188
6.2.2 Derivation of a basic structure for a Bayesian network 188
6.2.3 Stain found on a suspect s clothing 190
6.2.4 Fibres found on a car seat 191
6.2.5 The Background node 192
6.2.6 Background from different sources 194
6.2.7 A note on the Match node 198
6.2.8 A match considered in terms of components y and x 198
x CONTENTS
6.2.9 A structure for a Bayesian network 201
6.2.10 Evaluation of the proposed model 206
6.3 Cross- or two-way transfer of evidential material 207
6.4 Increasing the level of detail of selected nodes 210
6.5 Missing evidence 212
6.5.1 Determination of a structure for a Bayesian network 212
7 Aspects of the combination of evidence 215
7.1 Introduction 215
7.2 A difficulty in combining evidence 216
7.3 The likelihood ratio and the combination of evidence 217
7.3.1 Conditionally independent items of evidence 218
7.3.2 Conditionally non-independent items of evidence 219
7.4 Combination of distinct items of evidence 222
7.4.1 Example 1: Handwriting and fingermarks evidence 222
7.4.2 Example 2: Issues in DNA analysis 226
7.4.3 Example 3: Scenario with one offender and two corresponding items
of evidence 229
7.4.4 Example 4: Scenarios involving firearms 234
8 Pre-assessment 245
8.1 Introduction 245
8.2 Pre-assessment 246
8.3 Pre-assessment for a fibres scenario 247
8.3.1 Preliminaries 247
8.3.2 Propositions and relevant events 248
8.3.3 Expected likelihood ratios 250
8.3.4 Construction of a Bayesian network 253
8.4 Pre-assessment in a cross-transfer scenario 253
8.4.1 Preliminaries 255
8.4.2 A Bayesian network for a pre-assessment of a cross-transfer
scenario 255
8.4.3 The expected weight of evidence 257
8.5 Pre-assessment with multiple propositions 260
8.5.1 Preliminaries 260
8.5.2 Construction of a Bayesian network 261
8.5.3 Evaluation of different scenarios 263
8.5.4 An alternative graphical structure 265
8.6 Remarks 266
9 Qualitative and sensitivity analyses 269
9.1 Qualitative probability models 270
9.1.1 Qualitative influence 270
9.1.2 Additive synergy 273
9.1.3 Product synergy 274
9.1.4 Properties of qualitative relationships 277
CONTENTS xi
9.1.5 Evaluation of indirect influences between separated nodes: a forensic
example 279
9.1.6 Implications of qualitative graphical models 284
9.2 Sensitivity analyses 285
9.2.1 Sensitivity to a single parameter 286
9.2.2 One-way sensitivity analysis based on a likelihood ratio 288
9.2.3 A further example of one-way sensitivity analysis 288
9.2.4 Sensitivity to two parameters 290
9.2.5 Further issues in sensitivity analyses 292
10 Continuous networks 295
10.1 Introduction 295
10.2 Samples and estimates 295
10.3 Measurements 296
10.3.1 Summary statistics 296
10.3.2 Normal distribution 297
10.3.3 Propagation in a continuous Bayesian network 301
10.3.4 Propagation in mixed networks 307
10.3.5 Example of mixed network 307
10.4 Use of a continuous distribution which is not normal 309
10.5 Appendix 313
10.5.1 Conditional expectation and variance 313
10.5.2 Bayesian network for three serially connected continuous variables . 314
10.5.3 Bayesian network for a continuous variable with a binary parent . . 316
10.5.4 Bayesian network for a continuous variable with a continuous parent
and a binary parent, unmarried 317
11 Further applications 319
11.1 Offender profiling 319
11.2 Decision making 322
11.2.1 Decision analysis 323
11.2.2 Bayesian networks and decision networks 324
11.2.3 Forensic decision analyses 328
Bibliography 331
Author Index 341
Subject Index 345
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| spelling | Bayesian networks and probabilistic inference in forensic science Franco Taroni ... Chichester [u.a.] Wiley 2006 XVIII, 354 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series on statistics in practice Includes bibliographical references and indexes The logic of uncertainty -- The logic of Bayesian networks -- Evaluation of scientific evidence -- Bayesian networks for evaluating scientific evidence -- DNA evidence -- Transfer evidence -- Aspects of the combination of evidence -- Pre-assessment -- Qualitative and sensitivity analyses -- Continuous networks -- Further applications Bayesian statistical decision theory / Graphic methods Uncertainty (Information theory) / Graphic methods Forensic sciences / Graphic methods Criminalistique - Méthodes graphiques Incertitude (Théorie de l'information) - Méthodes graphiques Statistique bayésienne - Méthodes graphiques Bayesian statistical decision theory Graphic methods Forensic sciences Graphic methods Uncertainty (Information theory) Graphic methods Kriminaltechnik (DE-588)4134284-7 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 s Kriminaltechnik (DE-588)4134284-7 s DE-604 Taroni, Franco Sonstige oth http://www.loc.gov/catdir/enhancements/fy0658/2005057711-d.html Publisher description kostenfrei DE-605 pdf/application http://www.gbv.de/dms/hbz/toc/ht014696644.pdf 2008-11-15 Inhaltsverzeichnis HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017348710&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bayesian networks and probabilistic inference in forensic science Bayesian statistical decision theory / Graphic methods Uncertainty (Information theory) / Graphic methods Forensic sciences / Graphic methods Criminalistique - Méthodes graphiques Incertitude (Théorie de l'information) - Méthodes graphiques Statistique bayésienne - Méthodes graphiques Bayesian statistical decision theory Graphic methods Forensic sciences Graphic methods Uncertainty (Information theory) Graphic methods Kriminaltechnik (DE-588)4134284-7 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| subject_GND | (DE-588)4134284-7 (DE-588)4144220-9 |
| title | Bayesian networks and probabilistic inference in forensic science |
| title_auth | Bayesian networks and probabilistic inference in forensic science |
| title_exact_search | Bayesian networks and probabilistic inference in forensic science |
| title_full | Bayesian networks and probabilistic inference in forensic science Franco Taroni ... |
| title_fullStr | Bayesian networks and probabilistic inference in forensic science Franco Taroni ... |
| title_full_unstemmed | Bayesian networks and probabilistic inference in forensic science Franco Taroni ... |
| title_short | Bayesian networks and probabilistic inference in forensic science |
| title_sort | bayesian networks and probabilistic inference in forensic science |
| topic | Bayesian statistical decision theory / Graphic methods Uncertainty (Information theory) / Graphic methods Forensic sciences / Graphic methods Criminalistique - Méthodes graphiques Incertitude (Théorie de l'information) - Méthodes graphiques Statistique bayésienne - Méthodes graphiques Bayesian statistical decision theory Graphic methods Forensic sciences Graphic methods Uncertainty (Information theory) Graphic methods Kriminaltechnik (DE-588)4134284-7 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| topic_facet | Bayesian statistical decision theory / Graphic methods Uncertainty (Information theory) / Graphic methods Forensic sciences / Graphic methods Criminalistique - Méthodes graphiques Incertitude (Théorie de l'information) - Méthodes graphiques Statistique bayésienne - Méthodes graphiques Bayesian statistical decision theory Graphic methods Forensic sciences Graphic methods Uncertainty (Information theory) Graphic methods Kriminaltechnik Bayes-Entscheidungstheorie |
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