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Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science Author Franco Taroni
ISBN-10 9781118914748
Release 2014-07-21
Pages 472
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"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.



Data Analysis in Forensic Science

Data Analysis in Forensic Science Author Franco Taroni
ISBN-10 0470665076
Release 2010-03-19
Pages 388
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This is the first text to examine the use of statistical methods in forensic science and bayesian statistics in combination. The book is split into two parts: Part One concentrates on the philosophies of statistical inference. Chapter One examines the differences between the frequentist, the likelihood and the Bayesian perspectives, before Chapter Two explores the Bayesian decision-theoretic perspective further, and looks at the benefits it carries. Part Two then introduces the reader to the practical aspects involved: the application, interpretation, summary and presentation of data analyses are all examined from a Bayesian decision-theoretic perspective. A wide range of statistical methods, essential in the analysis of forensic scientific data is explored. These include the comparison of allele proportions in populations, the comparison of means, the choice of sampling size, and the discrimination of items of evidence of unknown origin into predefined populations. Throughout this practical appraisal there are a wide variety of examples taken from the routine work of forensic scientists. These applications are demonstrated in the ever-more popular R language. The reader is taken through these applied examples in a step-by-step approach, discussing the methods at each stage.



Bayesian Networks and Probabilistic Inference in Forensic Science

Bayesian Networks and Probabilistic Inference in Forensic Science Author Franco Taroni
ISBN-10 0470091738
Release 2006-04-07
Pages 372
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The amount of information forensic scientists are able to offer is ever increasing, owing to vast developments in science and technology. Consequently, the complexity of evidence does not allow scientists to cope adequately with the problems it causes, or to make the required inferences. Probability theory, implemented through graphical methods, specifically Bayesian networks, offers a powerful tool to deal with this complexity, and discover valid patterns in data. Bayesian Networks and Probabilistic Inference in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian networks for the evaluation of scientific evidence in forensic science. Includes self-contained introductions to both Bayesian networks and probability. Features implementation of the methodology using HUGIN, the leading Bayesian networks software. Presents basic standard networks that can be implemented in commercially and academically available software packages, and that form the core models necessary for the reader’s own analysis of real cases. Provides a technique for structuring problems and organizing uncertain data based on methods and principles of scientific reasoning. Contains a method for constructing coherent and defensible arguments for the analysis and evaluation of forensic evidence. Written in a lucid style, suitable for forensic scientists with minimal mathematical background. Includes a foreword by David Schum. The clear and accessible style makes this book ideal for all forensic scientists and applied statisticians working in evidence evaluation, as well as graduate students in these areas. It will also appeal to scientists, lawyers and other professionals interested in the evaluation of forensic evidence and/or Bayesian networks.



Introduction to Statistics for Forensic Scientists

Introduction to Statistics for Forensic Scientists Author David Lucy
ISBN-10 9781118700105
Release 2013-05-03
Pages 272
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Introduction to Statistics for Forensic Scientists is an essential introduction to the subject, gently guiding the reader through the key statistical techniques used to evaluate various types of forensic evidence. Assuming only a modest mathematical background, the book uses real-life examples from the forensic science literature and forensic case-work to illustrate relevant statistical concepts and methods. Opening with a brief overview of the history and use of statistics within forensic science, the text then goes on to introduce statistical techniques commonly used to examine data obtained during laboratory experiments. There is a strong emphasis on the evaluation of scientific observation as evidence and modern Bayesian approaches to interpreting forensic data for the courts. The analysis of key forms of evidence are discussed throughout with a particular focus on DNA, fibres and glass. An invaluable introduction to the statistical interpretation of forensic evidence; this book will be invaluable for all undergraduates taking courses in forensic science. Introduction to the key statistical techniques used in the evaluation of forensic evidence Includes end of chapter exercises to enhance student understanding Numerous examples taken from forensic science to put the subject into context



Introduction to Data Analysis with R for Forensic Scientists

Introduction to Data Analysis with R for Forensic Scientists Author James Michael Curran
ISBN-10 1420088270
Release 2010-07-30
Pages 331
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Statistical methods provide a logical, coherent framework in which data from experimental science can be analyzed. However, many researchers lack the statistical skills or resources that would allow them to explore their data to its full potential. Introduction to Data Analysis with R for Forensic Sciences minimizes theory and mathematics and focuses on the application and practice of statistics to provide researchers with the dexterity necessary to systematically analyze data discovered from the fruits of their research. Using traditional techniques and employing examples and tutorials with real data collected from experiments, this book presents the following critical information necessary for researchers: A refresher on basic statistics and an introduction to R Considerations and techniques for the visual display of data through graphics An overview of statistical hypothesis tests and the reasoning behind them A comprehensive guide to the use of the linear model, the foundation of most statistics encountered An introduction to extensions to the linear model for commonly encountered scenarios, including logistic and Poisson regression Instruction on how to plan and design experiments in a way that minimizes cost and maximizes the chances of finding differences that may exist Focusing on forensic examples but useful for anyone working in a laboratory, this volume enables researchers to get the most out of their experiments by allowing them to cogently analyze the data they have collected, saving valuable time and effort.



Bayesian Networks

Bayesian Networks Author Olivier Pourret
ISBN-10 0470994541
Release 2008-04-30
Pages 446
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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.



Risk Assessment and Decision Analysis with Bayesian Networks Second Edition

Risk Assessment and Decision Analysis with Bayesian Networks  Second Edition Author Norman Fenton
ISBN-10 9781351978965
Release 2018-08-08
Pages 704
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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.



Crossover Designs

Crossover Designs Author Kung-Jong Lui
ISBN-10 9781119114680
Release 2016-09-26
Pages 248
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Crossover Designs: Testing, Estimation and Sample Size Kung-Jong Lui, Department of Mathematics and Statistics, San Diego State University, USA A comprehensive and practical resource for analyses of crossover designs For ethical reasons, it is vital to keep the number of patients in a clinical trial as low as possible. As evidenced by extensive research publications, crossover design can be a useful and powerful tool to reduce the number of patients needed for a parallel group design in studying treatments for non-curable chronic diseases. This book introduces commonly-used and well-established statistical tests and estimators in epidemiology that can easily be applied to hypothesis testing and estimation of the relative treatment effect for various types of data scale in crossover designs. Models with distribution-free random effects are assumed and hence most approaches considered here are semi-parametric. The book provides clinicians and biostatisticians with the exact test procedures and exact interval estimators, which are applicable even when the number of patients in a crossover trial is small. Systematic discussion on sample size determination is also included, which will be a valuable resource for researchers involved in crossover trial design. Key features: l Provides exact test procedures and interval estimators, which are especially of use in small-sample cases. l Presents most test procedures and interval estimators in closed-forms, enabling readers to calculate them by use of a pocket calculator or commonly-used statistical packages. l Each chapter is self-contained, allowing the book to be used a reference resource. l Uses real-life examples to illustrate the practical use of test procedures and estimators l Provides extensive exercises to help readers appreciate the underlying theory, learn other relevant test procedures and understand how to calculate the required sample size. Crossover Designs: Testing, Estimation and Sample Size will be a useful resource for researchers from biostatistics, as well as pharmaceutical and clinical sciences. It can also be used as a textbook or reference for graduate students studying clinical experiments.



Statistics and the Evaluation of Evidence for Forensic Scientists

Statistics and the Evaluation of Evidence for Forensic Scientists Author Colin Aitken
ISBN-10 9780470011225
Release 2004-11-19
Pages 540
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The first edition of Statistics and the Evaluation of Evidence for Forensic Scientists established itself as a highly regarded authority on this area. Fully revised and updated, the second edition provides significant new material on areas of current interest including: Glass Interpretation Fibres Interpretation Bayes’ Nets The title presents comprehensive coverage of the statistical evaluation of forensic evidence. It is written with the assumption of a modest mathematical background and is illustrated throughout with up-to-date examples from a forensic science background. The clarity of exposition makes this book ideal for all forensic scientists, lawyers and other professionals in related fields interested in the quantitative assessment and evaluation of evidence. 'There can be no doubt that the appreciation of some evidence in a court of law has been greatly enhanced by the sound use of statistical ideas and one can be confident that the next decade will see further developments, during which time this book will admirably serve those who have cause to use statistics in forensic science.' D.V. Lindley



Bayesian Networks and Decision Graphs

Bayesian Networks and Decision Graphs Author Thomas Dyhre Nielsen
ISBN-10 0387682813
Release 2007-06-06
Pages 448
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This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems.



Encyclopedia of Forensic Sciences

Encyclopedia of Forensic Sciences Author
ISBN-10 9780123821669
Release 2012-12-28
Pages 2250
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Forensic science includes all aspects of investigating a crime, including: chemistry, biology and physics, and also incorporates countless other specialties. Today, the service offered under the guise of "forensic science’ includes specialties from virtually all aspects of modern science, medicine, engineering, mathematics and technology. The Encyclopedia of Forensic Sciences, Second Edition is a reference source that will inform both the crime scene worker and the laboratory worker of each other’s protocols, procedures and limitations. Written by leading scientists in each area, every article is peer reviewed to establish clarity, accuracy, and comprehensiveness. As reflected in the specialties of its Editorial Board, the contents covers the core theories, methods and techniques employed by forensic scientists – and applications of these that are used in forensic analysis. This 4-volume set represents a 30% growth in articles from the first edition, with a particular increase in coverage of DNA and digital forensics Includes an international collection of contributors The second edition features a new 21-member editorial board, half of which are internationally based Includes over 300 articles, approximately 10pp on average Each article features a) suggested readings which point readers to additional sources for more information, b) a list of related Web sites, c) a 5-10 word glossary and definition paragraph, and d) cross-references to related articles in the encyclopedia Available online via SciVerse ScienceDirect. Please visit www.info.sciencedirect.com for more information This new edition continues the reputation of the first edition, which was awarded an Honorable Mention in the prestigious Dartmouth Medal competition for 2001. This award honors the creation of reference works of outstanding quality and significance, and is sponsored by the RUSA Committee of the American Library Association



Bayesian Networks and BayesiaLab

Bayesian Networks and BayesiaLab Author Stefan Conrady
ISBN-10 0996533303
Release 2015-07-01
Pages
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Bayesian Networks and BayesiaLab has been writing in one form or another for most of life. You can find so many inspiration from Bayesian Networks and BayesiaLab also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Bayesian Networks and BayesiaLab book for free.



Information Theory Inference and Learning Algorithms

Information Theory  Inference and Learning Algorithms Author David J. C. MacKay
ISBN-10 0521642981
Release 2003-09-25
Pages 628
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Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.



Forensics

Forensics Author Anita Yasuda
ISBN-10 9781619303485
Release 2016-03-21
Pages 112
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How do scientists solve mysteries? With forensics! Every crime scene contains forensic evidence that helps investigators discover exactly what happened. Forensics is the science of gathering and examining information about a past event, usually to solve a crime or legal problem. In Forensics: Cool Women Who Investigate, children ages 9 through 12 learn about this fascinating field and meet three women who are succeeding in their chosen profession of forensics. Christine Gabig-Prebyl is a forensic scientist with Douglas County Sheriff’s Office, Krishna Patel is a Forensic Supervisor with the Torrance Police Department, and Jessica Frances Lam is a researcher at England’s University of Leicester INTREPID Forensics Programme. Forensics combines high-interest content with links to online primary sources and essential questions that further expand kids’ knowledge and understanding of a topic made popular by TV shows, movies, and books. Compelling stories of real-life forensic scientists provide role models that readers can look toward for examples of success. Nomad Press books in the Girls in Science series supply a bridge between girls’ interests and their potential futures by investigating science careers and introducing women who have succeeded in science.



Bayesian Networks

Bayesian Networks Author Timo Koski
ISBN-10 9781119964957
Release 2011-08-26
Pages 366
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Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout. Features include: An introduction to Dirichlet Distribution, Exponential Families and their applications. A detailed description of learning algorithms and Conditional Gaussian Distributions using Junction Tree methods. A discussion of Pearl's intervention calculus, with an introduction to the notion of see and do conditioning. All concepts are clearly defined and illustrated with examples and exercises. Solutions are provided online. This book will prove a valuable resource for postgraduate students of statistics, computer engineering, mathematics, data mining, artificial intelligence, and biology. Researchers and users of comparable modelling or statistical techniques such as neural networks will also find this book of interest.



Handbook on Neural Information Processing

Handbook on Neural Information Processing Author Monica Bianchini
ISBN-10 9783642366574
Release 2013-04-12
Pages 538
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This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformatics This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.



Bayesian Reasoning in Data Analysis

Bayesian Reasoning in Data Analysis Author Giulio D'Agostini
ISBN-10 9789812383563
Release 2003
Pages 329
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A multi-level introduction to Bayesian reasoning. The basic ideas of this approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference; combination of results; comparison of hypotheses; and more.