Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science, 2nd Edition

Book description

"This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation"

Dr. Ian Evett, Principal Forensic Services Ltd, London, UK

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing.

The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision.

Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks.

  • Includes self-contained introductions to probability and decision theory.

  • Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models.

  • Features implementation of the methodology with reference to commercial and academically available software.

  • Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases.

  • Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning.

  • Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them.

  • Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background.

  • Includes a foreword by Ian Evett.

  • The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

    Table of contents

    1. Cover
    2. Statistics In Practice
    3. Title Page
    4. Copyright
    5. Dedication
    6. Foreword
    7. Preface to the second edition
    8. Preface to the first edition
    9. Chapter 1: The logic of decision
      1. 1.1 Uncertainty and probability
      2. 1.2 Reasoning under uncertainty
      3. 1.3 Population proportions, probabilities and induction
      4. 1.4 Decision making under uncertainty
      5. 1.5 Further readings
    10. Chapter 2: The logic of Bayesian networks and influence diagrams
      1. 2.1 Reasoning with graphical models
      2. 2.2 Reasoning with Bayesian networks and influence diagrams
      3. 2.3 Further readings
    11. Chapter 3: Evaluation of scientific findings in forensic science
      1. 3.1 Introduction
      2. 3.2 The value of scientific findings
      3. 3.3 Principles of forensic evaluation and relevant propositions
      4. 3.4 Pre-assessment of the case
      5. 3.5 Evaluation using graphical models
    12. Chapter 4: Evaluation given source level propositions
      1. 4.1 General considerations
      2. 4.2 Standard statistical distributions
      3. 4.3 Two stains, no putative source
      4. 4.4 Multiple propositions
    13. Chapter 5: Evaluation given activity level propositions
      1. 5.1 Evaluation of transfer material given activity level propositions assuming a direct source relationship
      2. 5.2 Cross- or two-way transfer of trace material
      3. 5.3 Evaluation of transfer material given activity level propositions with uncertainty about the true source
    14. Chapter 6: Evaluation given crime level propositions
      1. 6.1 Material found on a crime scene: A general approach
      2. 6.2 Findings with more than one component: The example of marks
      3. 6.3 Scenarios with more than one trace: ‘Two stain-one offender’ cases
      4. 6.4 Material found on a person of interest
    15. Chapter 7: Evaluation of DNA profiling results
      1. 7.1 DNA likelihood ratio
      2. 7.2 Network approaches to the DNA likelihood ratio
      3. 7.3 Missing suspect
      4. 7.4 Analysis when the alternative proposition is that a brother of the suspect left the crime stain
      5. 7.5 Interpretation with more than two propositions
      6. 7.6 Evaluation with more than two propositions
      7. 7.7 Partially corresponding profiles
      8. 7.8 Mixtures
      9. 7.9 Kinship analyses
      10. 7.10 Database search
      11. 7.11 Probabilistic approaches to laboratory error
      12. 7.12 Further reading
    16. Chapter 8: Aspects of combining evidence
      1. 8.1 Introduction
      2. 8.2 A difficulty in combining evidence: The ‘problem of conjunction’
      3. 8.3 Generic patterns of inference in combining evidence
      4. 8.4 Examples of the combination of distinct items of evidence
    17. Chapter 9: Networks for continuous models
      1. 9.1 Random variables and distribution functions
      2. 9.2 Samples and estimates
      3. 9.3 Continuous Bayesian networks
      4. 9.4 Mixed networks
    18. Chapter 10: Pre-assessment
      1. 10.1 Introduction
      2. 10.2 General elements of pre-assessment
      3. 10.3 Pre-assessment in a fibre case: A worked through example
      4. 10.4 Pre-assessment in a cross-transfer scenario
      5. 10.5 Pre-assessment for consignment inspection
      6. 10.6 Pre-assessment for gunshot residue particles
    19. Chapter 11: Bayesian decision networks
      1. 11.1 Decision making in forensic science
      2. 11.2 Examples of forensic decision analyses
      3. 11.3 Further readings
    20. Chapter 12: Object-oriented networks
      1. 12.1 Object orientation
      2. 12.2 General elements of object-oriented networks
      3. 12.3 Object-oriented networks for evaluating DNA profiling results
    21. Chapter 13: Qualitative, sensitivity and conflict analyses
      1. 13.1 Qualitative probability models
      2. 13.2 Sensitivity analyses
      3. 13.3 Conflict analysis
    22. References
    23. Author index
    24. Subject index
    25. Statistics in Practice
    26. End User License Agreement

    Product information

    • Title: Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science, 2nd Edition
    • Author(s):
    • Release date: September 2014
    • Publisher(s): Wiley
    • ISBN: 9780470979730