| Description |
xxiv, 443 pages ; 25 cm |
| Series |
Statistics in practice |
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Statistics in practice.
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| Contents |
Contents note continued: 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 -- 1.4.3.The rule of maximizing expected utility -- 1.4.4.The loss function -- 1.4.5.Decision trees -- 1.4.6.The expected value of information -- 1.5.Further readings -- 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 -- 2.1.10.Conditional independence in influence diagrams -- 2.1.11.Relevance and causality -- 2.1.12.The Hound of the Baskervilles revisited -- |
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Contents note continued: 10.5.2.Bayesian network for inference about small consignments -- 10.5.3.Pre-assessment for inspection of small consignments -- 10.6.Pre-assessment for gunshot residue particles -- 10.6.1.Formation and deposition of gunshot residue particles -- 10.6.2.Bayesian network for grouped expected findings (GSR counts) -- 10.6.3.Examples for GSR count pre-assessment using a Bayesian network -- 11.Bayesian decision networks -- 11.1.Decision making in forensic science -- 11.2.Examples of forensic decision analyses -- 11.2.1.Deciding about whether or not to perform a DNA analysis -- 11.2.2.Probability assignment as a question of decision making -- 11.2.3.Decision analysis for consignment inspection -- 11.2.4.Decision after database searching -- 11.3.Further readings -- 12.Object-oriented networks -- 12.1.Object orientation -- 12.2.General elements of object-oriented networks -- 12.2.1.Static versus dynamic networks -- |
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Contents note continued: 12.2.2.Dynamic Bayesian networks as object-oriented networks -- 12.2.3.Refining internal class descriptions -- 12.3.Object-oriented networks for evaluating DNA profiling results -- 12.3.1.Basic disputed paternity case -- 12.3.2.Useful class networks for modelling kinship analyses -- 12.3.3.Object-oriented networks for kinship analyses -- 12.3.4.Object-oriented networks for inference of source -- 12.3.5.Refining internal class descriptions and further considerations -- 13.Qualitative, sensitivity and conflict analyses -- 13.1.Qualitative probability models -- 13.1.1.Qualitative influence -- 13.1.2.Additive synergy -- 13.1.3.Product synergy -- 13.1.4.Properties of qualitative relationships -- 13.1.5.Implications of qualitative graphical models -- 13.2.Sensitivity analyses -- 13.2.1.Preliminaries -- 13.2.2.Sensitivity to a single probability assignment -- 13.2.3.Sensitivity to two probability assignments -- 13.2.4.Sensitivity to prior distribution -- |
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Contents note continued: 13.3.Conflict analysis -- 13.3.1.Conflict detection -- 13.3.2.Tracing a conflict -- 13.3.3.Conflict resolution |
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Contents note continued: 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 -- 3.Evaluation of scientific findings in forensic science -- 3.1.Introduction -- 3.2.The value of scientific findings -- 3.3.Principles of forensic evaluation and relevant propositions -- 3.3.1.Source level propositions -- 3.3.2.Activity level propositions -- 3.3.3.Crime level propositions -- 3.4.Pre-assessment of the case -- 3.5.Evaluation using graphical models -- 3.5.1.Introduction -- 3.5.2.General aspects of the construction of Bayesian networks -- 3.5.3.Eliciting structural relationships -- |
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Contents note continued: 3.5.4.Level of detail of variables and quantification of influences -- 3.5.5.Deriving an alternative network structure -- 4.Evaluation given source level propositions -- 4.1.General considerations -- 4.2.Standard statistical distributions -- 4.3.Two stains, no putative source -- 4.3.1.Likelihood ratio for source inference when no putative source is available -- 4.3.2.Bayesian network for a two-trace case with no putative source -- 4.3.3.An alternative network structure for a two trace no putative source case -- 4.4.Multiple propositions -- 4.4.1.Form of the likelihood ratio -- 4.4.2.Bayesian networks for evaluation given multiple propositions -- 5.Evaluation given activity level propositions -- 5.1.Evaluation of transfer material given activity level propositions assuming a direct source relationship -- 5.1.1.Preliminaries -- 5.1.2.Derivation of a basic structure for a Bayesian network -- 5.1.3.Modifying the basic network -- |
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Contents note continued: 5.1.4.Further considerations about background presence -- 5.1.5.Background from different sources -- 5.1.6.An alternative description of the findings -- 5.1.7.Bayesian network for an alternative description of findings -- 5.1.8.Increasing the level of detail of selected propositions -- 5.1.9.Evaluation of the proposed model -- 5.2.Cross- or two-way transfer of trace material -- 5.3.Evaluation of transfer material given activity level propositions with uncertainty about the true source -- 5.3.1.Network structure -- 5.3.2.Evaluation of the network -- 5.3.3.Effect of varying assumptions about key factors -- 6.Evaluation given crime level propositions -- 6.1.Material found on a crime scene: A general approach -- 6.1.1.Generic network construction for single offender -- 6.1.2.Evaluation of the network -- 6.1.3.Extending the single-offender scenario -- 6.1.4.Multiple offenders -- 6.1.5.The role of the relevant population -- |
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Contents note continued: 6.2.Findings with more than one component: The example of marks -- 6.2.1.General considerations -- 6.2.2.Adding further propositions -- 6.2.3.Derivation of the likelihood ratio -- 6.2.4.Consideration of distinct components -- 6.2.5.An extension to firearm examinations -- 6.2.6.A note on the likelihood ratio -- 6.3.Scenarios with more than one trace: ̀Two stain-one offender' cases -- 6.4.Material found on a person of interest -- 6.4.1.General form -- 6.4.2.Extending the numerator -- 6.4.3.Extending the denominator -- 6.4.4.Extended form of the likelihood ratio -- 6.4.5.Network construction and examples -- 7.Evaluation of DNA profiling results -- 7.1.DNA likelihood ratio -- 7.2.Network approaches to the DNA likelihood ratio -- 7.2.1.The ̀match' approach -- 7.2.2.Representation of individual alleles -- 7.2.3.Alternative representation of a genotype -- 7.3.Missing suspect -- |
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Contents note continued: 7.12.1.A note on object-oriented Bayesian networks -- 7.12.2.Additional topics -- 8.Aspects of combining evidence -- 8.1.Introduction -- 8.2.A difficulty in combining evidence: The ̀problem of conjunction' -- 8.3.Generic patterns of inference in combining evidence -- 8.3.1.Preliminaries -- 8.3.2.Dissonant evidence: Contradiction and conflict -- 8.3.3.Harmonious evidence: Corroboration and convergence -- 8.3.4.Drag coefficient -- 8.4.Examples of the combination of distinct items of evidence -- 8.4.1.Handwriting and fingermarks -- 8.4.2.Issues in DNA analyses -- 8.4.3.One offender and two corresponding traces -- 8.4.4.Firearms and gunshot residues -- 8.4.5.Comments -- 9.Networks for continuous models -- 9.1.Random variables and distribution functions -- 9.1.1.Normal distribution -- 9.1.2.Bivariate Normal distribution -- 9.1.3.Conditional expectation and variance -- 9.2.Samples and estimates -- 9.2.1.Summary statistics -- 9.2.2.The Bayesian paradigm -- |
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Contents note continued: 7.4.Analysis when the alternative proposition is that a brother of the suspect left the crime stain -- 7.4.1.Revision of probabilities and networks -- 7.4.2.Further considerations on conditional genotype probabilities -- 7.5.Interpretation with more than two propositions -- 7.6.Evaluation with more than two propositions -- 7.7.Partially corresponding profiles -- 7.8.Mixtures -- 7.8.1.Considering multiple crime stain contributors -- 7.8.2.Bayesian network for a three-allele mixture scenario -- 7.9.Kinship analyses -- 7.9.1.A disputed paternity -- 7.9.2.An extended paternity scenario -- 7.9.3.A case of questioned maternity -- 7.10.Database search -- 7.10.1.Likelihood ratio after database searching -- 7.10.2.An analysis focussing on posterior probabilities -- 7.11.Probabilistic approaches to laboratory error -- 7.11.1.Implicit approach to typing error -- 7.11.2.Explicit approach to typing error -- 7.12.Further reading -- |
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Contents note continued: 9.3.Continuous Bayesian networks -- 9.3.1.Propagation in a continuous Bayesian network -- 9.3.2.Background data -- 9.3.3.Intervals for a continuous entity -- 9.4.Mixed networks -- 9.4.1.Bayesian network for a continuous variable with a discrete parent -- 9.4.2.Bayesian network for a continuous variable with a continuous parent and a binary parent, unmarried -- 10.Pre-assessment -- 10.1.Introduction -- 10.2.General elements of pre-assessment -- 10.3.Pre-assessment in a fibre case: A worked through example -- 10.3.1.Preliminaries -- 10.3.2.Propositions and relevant events -- 10.3.3.Expected likelihood ratios -- 10.3.4.Construction of a Bayesian network -- 10.4.Pre-assessment in a cross-transfer scenario -- 10.4.1.Bidirectional transfer -- 10.4.2.A Bayesian network for a pre-assessment of a cross-transfer scenario -- 10.4.3.The value of the findings -- 10.5.Pre-assessment for consignment inspection -- 10.5.1.Inspecting small consignments -- |
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Machine generated contents note: 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 -- 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.Population proportions, probabilities and induction -- 1.3.1.The statistical syllogism -- 1.3.2.Expectations and population proportions -- 1.3.3.Probabilistic explanations -- |
| Notes |
Formerly CIP. Uk |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Bayesian statistical decision theory -- Graphic methods.
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Forensic sciences -- Graphic methods.
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Uncertainty (Information theory) -- Graphic methods.
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| ISBN |
9780470979730 (hbk.) |
