Bayesian Networks “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 Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science Second Edition 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 diffculties 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.

Bayesian Networks “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 Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science Second Edition 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 diffculties 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.

Since the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions. Features Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cybersecurity and more Introduces all necessary mathematics, probability, and statistics as needed Establishes the basics of probability, risk, and building and using Bayesian network models, before going into the detailed applications A dedicated website contains exercises and worked solutions for all chapters along with numerous other resources. The AgenaRisk software contains a model library with executable versions of all of the models in the book. Lecture slides are freely available to accredited academic teachers adopting the book on their course.

Statistics and Probability in Forensic Anthropology provides a practical guide for forensic scientists, primarily anthropologists and pathologists, on how to design studies, how to choose and apply statistical approaches, and how to interpret statistical outcomes in the forensic practice. As with other forensic, medical and biological disciplines, statistics have become increasingly important in forensic anthropology and legal medicine, but there is not a single book, which specifically addresses the needs of forensic anthropologists in relation to the research undertaken in the field and the interpretation of research outcomes and case findings within the setting of legal proceedings. The book includes the application of both frequentist and Bayesian statistics in relation to topics relevant for the research and the interpretation of findings in forensic anthropology, as well as general chapters on study design and statistical approaches addressing measurement errors and reliability. Scientific terminology understandable to students and advanced practitioners of forensic anthropology, pathology and related disciplines is used throughout. Additionally, Statistics and Probability in Forensic Anthropology facilitates sufficient understanding of the statistical procedures and data interpretation based on statistical outcomes and models, which helps the reader confidently present their work within the forensic context, either in the form of case reports for legal purposes or as research publications for the scientific community. - Contains the application of both frequentist and Bayesian statistics in relation to topics relevant for forensic anthropology research and the interpretation of findings - Provides examples of study designs and their statistical solutions, partly following the layout of scientific manuscripts on common topics in the field - Includes scientific terminology understandable to students and advanced practitioners of forensic anthropology, legal medicine and related disciplines

In order for forensic fibre examiners to fully utilize fibre and textile evidence during their analysis, they require not only specialised forensic knowledge but also in-depth knowledge of fibres, yarns and fabrics themselves. Production, both the chemical and physical structure, and the properties of these materials is required in order to determine the value of fibre evidence. This includes knowing production figures, fashion changes, sudden arrivals of new materials, dye variability, and numerous other factors that may have a bearing on the information obtained. Fully updated with the latest advances, Forensic Examination of Fibres, Third Edition continues in the tradition of the First (1992) and Second Editions (1999) as the premier text on the subject of forensic fibre analysis. The international team of contributing authors detail the recovery of the evidence—through the different stages of laboratory examination—to the evaluation of the meaning of findings. The coverage has been considerably expanded, and all material, has been revised and wholly updated. Topics covered include examining damaged textiles, infrared microspectroscopy and thin layer chomatography, and colour analyses. This edition also highlights the critical role of quality assurance in ensuring the reliability of the technical observations and results, and, in doing so, looks at the implications of supervisory managers and labs in the accurate and responsible analysis of such evidence. Features include: Outlining evidentiary process from collecting and preserving the evidence at the crime scene through the laboratory analysis of fibres Detailing the latest developments and emerging technologies including Kevlar and other such advances in fibre technology Coverage of a broad array of fibres both, natural (cellulose, protein, and mineral) and man-made fibres including synthetic, inorganic and regenerated Forensic Examination of Fibres, Third Edition is a much-needed update to the classic book, serving as an indispensable reference to crime scene technicians, laboratory forensic scientists and microscopists, students in police, forensic, and justice science programs.

Now in its second edition, Forensic DNA Evidence Interpretation is the most comprehensive resource for DNA casework available today. Written by leaders in the fields of biology and statistics, including a contribution from Peter Gill, the father of DNA analysis, the book emphasizes the interpretation of test results and provides the necessary formulae in an easily accessible manner. This latest edition is fully updated and includes current and emerging techniques in this fast-moving field. The book begins by reviewing all pertinent biology, and then provides information on every aspect of DNA analysis. This includes modern interpretation methods and contemporary population genetic models available for estimating DNA frequencies or likelihood ratios. Following a chapter on procedures for validating databases, the text presents overviews and performance assessments of both modern sampling uncertainty methods and current paternity testing techniques, including new guidelines on paternity testing in alignment with the International Society for Forensic Genetics. Later chapters discuss the latest methods for mixture analysis, LCN (ultra trace) analysis and non-autosomal (mito, X, and Y) DNA analysis. The text concludes with an overview of procedures for disaster victim identification and information on DNA intelligence databases. Highlights of the second edition include: New information about PCR processes, heterozygote balance and back and forward stuttering New information on the interpretation of low template DNA, drop models and continuous models Additional coverage of lineage marker subpopulation effects, mixtures and combinations with autosomal markers This authoritative book provides a link among the biological, forensic, and interpretative domains of the DNA profiling field. It continues to serve as an invaluable resource that allows forensic scientists, technicians, molecular biologists and attorneys to use forensic DNA evidence to its greatest potential.

Written by some major contributors to the development of this class of graphical models, Chain Event Graphs introduces a viable and straightforward new tool for statistical inference, model selection and learning techniques. The book extends established technologies used in the study of discrete Bayesian Networks so that they apply in a much more general setting As the first book on Chain Event Graphs, this monograph is expected to become a landmark work on the use of event trees and coloured probability trees in statistics, and to lead to the increased use of such tree models to describe hypotheses about how events might unfold. Features: introduces a new and exciting discrete graphical model based on an event tree focusses on illustrating inferential techniques, making its methodology accessible to a very broad audience and, most importantly, to practitioners illustrated by a wide range of examples, encompassing important present and future applications includes exercises to test comprehension and can easily be used as a course book introduces relevant software packages Rodrigo A. Collazo is a methodological and computational statistician based at the Naval Systems Analysis Centre (CASNAV) in Rio de Janeiro, Brazil. Christiane Görgen is a mathematical statistician at the Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. Jim Q. Smith is a professor of statistics at the University of Warwick, UK. He has published widely in the field of statistics, AI, and decision analysis and has written two other books, most recently Bayesian Decision Analysis: Principles and Practice (Cambridge University Press 2010).

Despite a shared focus on crime and its ‘extended family’, forensic scientists and criminologists tend to work in isolation rather than sharing the data, methods and knowledge that will broaden the understanding of the criminal phenomenon and its related subjects. Bringing together perspectives from international experts, this book explores the intersection between criminology and forensic science and considers how knowledge from both fields can contribute to a better understanding of crime and offer new directions in theory and methodology. This handbook is divided into three parts: Part I explores the epistemological and historical components of criminology and forensic science, focusing on their scientific and social origins. Part II considers how collaboration between these disciplines can bring about a better understanding of the organizations and institutions that react to crime, including the court, intelligence, prevention, crime scene investigation and policing. Part III discusses the phenomena and actors that produce crime, including a reflection on the methodological issues, challenges and rewards regarding the sharing of these two disciplines. The objective of this handbook is to stimulate a ‘new’ interdisciplinary take on the study of crime, to show how both forensic and criminological theories and knowledge can be combined to analyse crime problems and to open new methodological perspectives. It will be essential reading for students and researchers engaged with forensic science, criminology, criminal behaviour, criminal investigation, crime analysis and criminal justice.

Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.