عنوان

Bayesian networks for probabilistic inference and decision analysis in forensic science

(Number (ISBN

:9780470979730

Erroneous ISBN

9780470979730 (hardback)

Number

37932

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Bayesian networks for probabilistic inference and decision analysis in forensic science

General Material Designation

[electronic resources]

First Statement of Responsibility

\ Franco Taroni, Alex Biedermann, Silvia Bozza, Paolo Garbolino, Colin Aitken

Edition Statement

Second edition

Place of Publication, Distribution, etc.

; Hoboken, NJ

Name of Publisher, Distributor, etc.

: Wiley

Date of Publication, Distribution, etc.

, 2014

Specific Material Designation and Extent of Item

xxiv, 403 p.

Other Physical Details

:ill.

Text of Note

Index

Text of Note

Bibliography

Text of Note

Machine generated contents note: Preface to the first edition Preface to the second edition 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 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 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 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 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 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 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 of 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 focusing 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 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 301 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 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 Bi-directional transfer 10.4.2 A Bayesian network for a pre-assessment of a cross-transfer scenario 10.4.3 The expected value of the findings 10.5 Pre-assessment for consignment inspection 10.5.1 Inspecting small consignments 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 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 modeling 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 13.3 Conflict analysis 13.3.1 Conflict detection 13.3.2 Tracing a conflict 13.3.3 Conflict resolution Bibliography Author Index Subject Index .

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Text of Note

"The aim is to offer theoretical and practical elements to help solve the following questions"--

Bayesian statistical decision theory -- Graphic methods

Uncertainty (Information theory)-- Graphic methods

Forensic sciences-- Graphic methods

MATHEMATICS / Probability & Statistics / General.

نظریه تصمیم گیری آماری بیزی -- روش های گرافیکی

Taroni, Franco

Country

ایران

Date of Transaction

20190610140514.0

Cataloguing Rules (Descriptive Conventions))

rda

Electronic name

9780470979730.pdf

Uniform Resource Identifier

http://catalogimages.wiley.com/images/db/jimages/9780470979730.jpg

BL

279177

1

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