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.
Bayes Factors for Forensic Decision Analyses with R provides a self-contained introduction to computational Bayesian statistics using R. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability—keeping theoretical and philosophical justifications limited. It offers a balanced approach to three naturally interrelated topics: Probabilistic Inference - Relies on the core concept of Bayesian inferential statistics, to help practicing forensic scientists in the logical and balanced evaluation of the weight of evidence. Decision Making - Features how Bayes factors are interpreted in practical applications to help address questions of decision analysis involving the use of forensic science in the law. Operational Relevance - Combines inference and decision, backed up with practical examples and complete sample code in R, including sensitivity analyses and discussion on how to interpret results in context. Over the past decades, probabilistic methods have established a firm position as a reference approach for the management of uncertainty in virtually all areas of science, including forensic science, with Bayes' theorem providing the fundamental logical tenet for assessing how new information—scientific evidence—ought to be weighed. Central to this approach is the Bayes factor, which clarifies the evidential meaning of new information, by providing a measure of the change in the odds in favor of a proposition of interest, when going from the prior to the posterior distribution. Bayes factors should guide the scientist's thinking about the value of scientific evidence and form the basis of logical and balanced reporting practices, thus representing essential foundations for rational decision making under uncertainty. This book would be relevant to students, practitioners, and applied statisticians interested in inference and decision analyses in the critical field of forensic science. It could be used to support practical courses on Bayesian statistics and decision theory at both undergraduate and graduate levels, and will be of equal interest to forensic scientists and practitioners of Bayesian statistics for driving their evaluations and the use of R for their purposes. This book is Open Access.
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.
From the 1898 Martian Siege of Baltimore to a forlorn AI attack dog on the blue-star-scarred surface of MZ458-C and from the merc-manned Freestead Mayflower off the coast of post-apocalyptic Portland to the man-desperate shores of the Red Sea, here are seventeen science fiction stories of valiant endurance. These heroes battle conspiracies of usurpers, confront the unearned consequences of others’ willful lunacy, seek out buried truths at unbearable personal expense and endure the inhuman demands of digital rebellion in worlds innately hostile to truth and freedom. With original, never-before-published works from veteran authors, including William F. Wu, as well as emerging talents.
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 “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.
Bayes Factors for Forensic Decision Analyses with R provides a self-contained introduction to computational Bayesian statistics using R. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability—keeping theoretical and philosophical justifications limited. It offers a balanced approach to three naturally interrelated topics: Probabilistic Inference - Relies on the core concept of Bayesian inferential statistics, to help practicing forensic scientists in the logical and balanced evaluation of the weight of evidence. Decision Making - Features how Bayes factors are interpreted in practical applications to help address questions of decision analysis involving the use of forensic science in the law. Operational Relevance - Combines inference and decision, backed up with practical examples and complete sample code in R, including sensitivity analyses and discussion on how to interpret results in context. Over the past decades, probabilistic methods have established a firm position as a reference approach for the management of uncertainty in virtually all areas of science, including forensic science, with Bayes' theorem providing the fundamental logical tenet for assessing how new information—scientific evidence—ought to be weighed. Central to this approach is the Bayes factor, which clarifies the evidential meaning of new information, by providing a measure of the change in the odds in favor of a proposition of interest, when going from the prior to the posterior distribution. Bayes factors should guide the scientist's thinking about the value of scientific evidence and form the basis of logical and balanced reporting practices, thus representing essential foundations for rational decision making under uncertainty. This book would be relevant to students, practitioners, and applied statisticians interested in inference and decision analyses in the critical field of forensic science. It could be used to support practical courses on Bayesian statistics and decision theory at both undergraduate and graduate levels, and will be of equal interest to forensic scientists and practitioners of Bayesian statistics for driving their evaluations and the use of R for their purposes. This book is Open Access.
Philosophy has a strong presence in evidence law and the nature of evidence is a highly debated topic in both general and social epistemology; legal theorists working in the evidence law area draw on different underlying philosophical theories of knowledge, inference and probability. Core evidentiary concepts and principles, such as the presumption of innocence, standards of proof, and others, reply on moral and political philosophy for their understanding and interpretation. Written by leading scholars across the globe, this volume brings together philosophical debates on the nature and function of evidence, proof, and law of evidence. It presents a cross-disciplinary overview of central issues in the theory and methodology of legal evidence and covers a wide range of contemporary debates on topics such as truth, proof, economics, gender, and race. The volume covers different theoretical approaches to legal evidence, including the Bayesian approach, scenario theory and inference to the best explanation. Divided in to five parts, Philosophical Foundations of Evidence Law, covers different theoretical approaches to legal evidence, including the Bayesian approach, scenario theory and inference to the best explanation.
From the 1898 Martian Siege of Baltimore to a forlorn AI attack dog on the blue-star-scarred surface of MZ458-C and from the merc-manned Freestead Mayflower off the coast of post-apocalyptic Portland to the man-desperate shores of the Red Sea, here are seventeen science fiction stories of valiant endurance. These heroes battle conspiracies of usurpers, confront the unearned consequences of others’ willful lunacy, seek out buried truths at unbearable personal expense and endure the inhuman demands of digital rebellion in worlds innately hostile to truth and freedom. With original, never-before-published works from veteran authors, including William F. Wu, as well as emerging talents.
Convinced before the onset of Operation "Barbarossa" in June 1941 of both the ease, with which the Red Army would be defeated and the likelihood that the Soviet Union would collapse, the Nazi regime envisaged a radical and far-reaching occupation policy which would result in the political, economic and racial reorganization of the occupied Soviet territories and bring about the deaths of 'x million people' through a conscious policy of starvation. This study traces the step-by-step development of high-level planning for the occupation policy in the Soviet territories over a twelve-month period and establishes the extent to which the various political and economic plans were compatible.
Explains how to perform and analyze the results of the latest physicochemical methods With this book as their guide, readers have access to all the current information needed to thoroughly investigate and accurately determine a compound's pharmaceutical properties and their effects on drug absorption. The book emphasizes oral absorption, explaining all the physicochemical methods used today to analyze drug candidates. Moreover, the author provides expert guidance to help readers analyze the results of their studies in order to select the most promising drug candidates. This Second Edition has been thoroughly updated and revised, incorporating all the latest research findings, methods, and resources, including: Descriptions and applications of new PAMPA models, drawing on more than thirty papers published by the author's research group Two new chapters examining permeability and Caco-2/MDCK and permeability and the blood-brain barrier Expanded information and methods to support pKa determination New examples explaining the treatment of practically insoluble test compounds Additional case studies demonstrating the use of the latest physicochemical techniques New, revised, and expanded database tables throughout the book Well over 200 drawings help readers better understand difficult concepts and provide a visual guide to complex procedures. In addition, over 800 references serve as a gateway to the primary literature in the field, facilitating further research into all the topics covered in the book. This Second Edition is recommended as a reference for researchers in pharmaceutical R&D as well as in agrochemical, environmental, and other related areas of research. It is also recommended as a supplemental text for graduate courses in pharmaceutics.
Electrotherapy Explained is an excellent research-based exploration of the major types of electrophysical agents used in clinical practice, particularly human and also animal. For the fourth edition, two new authors join the writing team, presenting the latest information for today's clinicians. The text has been completely updated with a major rewrite of the material, particularly that on electrical stimulation. This book continues to focus on evidence: clinical and biophysical evidence that affects how and which electrotherapies may be of use clinically and when. The inclusion of biophysics as well as clinical evidence and principles of application, enables clinicians to move away from traditional 'recipe-based' approaches and rely more on their own clinical reasoning. The focus remains on humans but the relevance of the principles for using and applying different modalities is explained clearly, providing guidelines for clinicians across disciplines and specialties. Up to date research detailing the evidence both supportive and deprecatory for the use of each modality Written by experts from biophysics and the clinical domains Comprehensive and well referenced Clear and well chosen illustrations elucidate the text Text boxes and summary sections help to break down what is sometimes a complex subject into manageable and memorable chunks Contraindications and risks have been updated in light of the most recent research Three books for the price of one - the website (http://booksite.elsevier.com/9780750688437) contains the entire texts of 'Physical Principles Explained' by Low and Reed, and 'Biophysical Bases of Electrotherapy' by Ward. The text directs readers to the website for further reading at relevant points.
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