The Social Contexts of Research. Edited by Saad Z. Nagi and Ronald G. Corwin. Pp. xii + 409. (John Wiley: New York and London, August 1972.) £5.65.

Sociology of Science

Advantages and challenges of Bayesian networks in environmental modelling

Sherlock Holmes said "it has long been an axiom of mine that the little things are infinitely the most important", but never imagined that such a little thing, the DNA molecule, could become perhaps the most powerful single tool in the multifaceted fight against crime. Twenty years after the development of DNA fingerprinting, forensic DNA analysis is key to the conviction or exoneration of suspects and the identification of victims of crimes, accidents and disasters, driving the development of innovative methods in molecular genetics, statistics and the use of massive intelligence databases.

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Encoded evidence: DNA in forensic analysis

Prospection (Gilbert & Wilson, 2007), the representation of possible futures, is a ubiquitous feature of the human mind. Much psychological theory and practice, in contrast, has understood human action as determined by the past and viewed any such teleology (selection of action in light of goals) as a violation of natural law because the future cannot act on the present. Prospection involves no backward causation; rather, it is guidance not by the future itself but by present, evaluative representations of possible future states. These representations can be understood minimally as “If X, then Y” conditionals, and the process of prospection can be understood as the generation and evaluation of these conditionals. We review the history of the attempt to cast teleology out of science, culminating in the failures of behaviorism and psychoanalysis to account adequately for action without teleology. A wide range of evidence suggests that prospection is a central organizing feature of perception, cognition, affect, memory, motivation, and action. The authors speculate that prospection casts new light on why subjectivity is part of consciousness, what is “free” and “willing” in “free will,” and on mental disorders and their treatment. Viewing behavior as driven by the past was a powerful framework that helped create scientific psychology, but accumulating evidence in a wide range of areas of research suggests a shift in framework, in which navigation into the future is seen as a core organizing principle of animal and human behavior.

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Navigating Into the Future or Driven by the Past

Biological Basis for DNA Evidence, Peter Gill and John Buckleton Historical and Background Biology Understanding PCR Profiles A Framework for Interpreting Evidence, John Buckleton The Frequentist Approach The Logical Approach The Full Bayesian Approach A Possible Solution A Comparison of the Different Approaches Population Genetic Models, John Buckleton Product Rule Simulation Testing Discussion of the Product Rule and the Subpopulation Model A Complex Case Example - DNA Evidence and Orethral James Simpson Relatedness, John Buckleton and Christopher Triggs Conditional Probabilities Joint Probabilities The Unifying Formula The Effect of Linkage Validating Databases, John Buckleton Which Is the Relevant Population? Population Databases Validating the Population Genetic Model Estimating Q Descriptive Statistics for Databases Sampling Effects, John Buckleton and James Curran Bounds and a Level Methods for Assessing Sampling Uncertainty Minimum Allele Probabilities Discussion of the Appropriateness of Sampling Uncertainty Estimates Mixtures, Tim Clayton and John Buckleton Frequentist Approaches Bayesian Approaches Statistical Evaluation of Mixtures Low Copy Number, John Buckleton and Peter Gill Changes in LCN Profile Morphology The Interpretation of LCN Profiles Non-autosomal Forensic Markers, Simon Walsh, SallyAnn Harbison, and John Buckleton Forensic Mitochondrial DNA Typing Forensic Y Chromosome Analysis Forensic X Chromosome Analysis A Famous Case Example - The Romanovs Parentage Testing, John Buckleton, Tim Clayton, and Chris Triggs Evaluation Of Evidence Paternity Trios: Mother, Child and Alleged Father Non-autosomal DNA Use of the Sub-Population Model of Balding and Nichols to Evaluate the Paternity Index Relatedness in Paternity Cases Multiple Children Inconsistencies in the Mendelian Pattern 'Exclusions' Paternity Trios: Mother, Child and Alleged Father Considering the Possibility of Silent (Null) Alleles Disaster Victim Identification, Identification of Missing Persons, and Immigration Cases, John Buckleton, Chris Triggs, and Tim Clayton Mitochondrial or Nuclear DNA? Human Remains - Obtaining a Profile from Bodily Remains Extraction of DNA from Bone, Tooth, Hair and Nail Complicating Factors DNA Intelligence Databases, Simon Walsh and John Buckleton A Brief History Functional Aspects Legislation Aspects of Forensic Significance Social and ethical considerations Interpretation Issues Associated with DNA Databases

Forensic DNA Evidence Interpretation

The logic of decision

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.

Bayesian Networks and Probabilistic Inference in Forensic Science

Bayesian Networks and Probabilistic Inference in Forensic Science: Taroni/Bayesian Networks and Probabilistic Inference in Forensic Science

Foreword. Preface. I The Foundations of Inference and Decision in Forensic Science. 1 Introduction. 1.1 The Inevitability of Uncertainty. 1.2 Desiderata in Evidential Assessment. 1.3 The Importance of the Propositional Framework and the Nature of Evidential Assessment. 1.4 From Desiderata to Applications. 1.5 The Bayesian Core of Forensic Science. 1.6 Structure of the Book. 2 Scientific Reasoning and Decision Making. 2.1 Coherent Reasoning Under Uncertainty. 2.2 Coherent Decision Making Under Uncertainty of Reasoning. 2.3 Scientific Reasoning as Coherent Decision Making. 2.4 Forensic Reasoning as Coherent Decision Making. 3 Concepts of Statistical Science and Decision Theory. 3.1 Random Variables and Distribution Functions. 3.2 Statistical Inference and Decision Theory. 3.3 The Bayesian Paradigm. 3.4 Bayesian Decision Theory. 3.5 R Code. II Forensic Data Analysis. 4 Point Estimation. 4.1 Introduction. 4.2 Bayesian Decision for a Proportion. 4.3 Bayesian Decision for a Poisson Mean. 4.4 Bayesian Decision for Normal Mean. 4.5 R Code. 5 Credible Intervals. 5.1 Introduction. 5.2 Credible Intervals. 5.3 Decision-Theoretic Evaluation of Credible Intervals. 5.4 R Code. 6 Hypothesis Testing. 6.1 Introduction. 6.2 Bayesian Hypothesis Testing. 6.3 One-sided testing. 6.4 Two-Sided Testing. 6.5 R Code. 7 Sampling. 7.1 Introduction. 7.2 Sampling Inspection. 7.3 Graphical Models for Sampling Inspection. 7.4 Sampling Inspection under a Decision-Theoretic Approach. 7.5 R Code. 8 Classification of Observations. 8.1 Introduction. 8.2 Standards of Coherent Classification. 8.3 Comparing Models using Discrete Data. 8.4 Comparison of Models using Continuous Data. 8.5 Non-Normal Distributions and Cocaine on Bank Notes. 8.6 A note on Multivariate Continuous Data. 8.7 R Code. 9 Bayesian Forensic Data Analysis: Conclusions and Implications. 9.1 Introduction. 9.2 What is the Past and Current Position of Statistics in Forensic Science? 9.3 Why Should Forensic Scientists Conform to a Bayesian Framework for Inference and Decision Making? 9.4 Why Regard Probability as a Personal Degree of Belief? 9.5 Why Should Scientists be Aware of Decision Analysis? 9.6 How to Implement Bayesian Inference and Decision Analysis? A Discrete Distributions. B Continuous Distributions. Bibliography. Author Index. Subject Index.

Data Analysis in Forensic Science: A Bayesian Decision Perspective

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science: Taroni/Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Paolo Garbolino

Papers

The logic of decision

Bayesian Networks and Probabilistic Inference in Forensic Science

Bayesian Networks and Probabilistic Inference in Forensic Science: Taroni/Bayesian Networks and Probabilistic Inference in Forensic Science

Data Analysis in Forensic Science: A Bayesian Decision Perspective

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science: Taroni/Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science