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. 
<ul>
<li>Includes self-contained introductions to both Bayesian networks and probability.</li> <li>Features implementation of the methodology using HUGIN, the leading Bayesian networks software.</li> <li>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.</li> <li>Provides a technique for structuring problems and organizing uncertain data based on methods and principles of scientific reasoning.</li> <li>Contains a method for constructing coherent and defensible arguments for the analysis and evaluation of forensic evidence.</li> <li>Written in a lucid style, suitable for forensic scientists with minimal mathematical background.</li> <li>Includes a foreword by David Schum.</li> </ul>
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.

Bayesian Networks and Probabilistic Inference in Forensic Science

Science & Math

Taroni, Franco

Biedermann, Alex

Garbolino, Paolo

Aitken, Colin

Mathematics

wiley

"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.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science (Statistics in Practice)

Bozza, Silvia

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.

Data Analysis in Forensic Science: A Bayesian Decision Perspective

Bayes Factors for Forensic Decision Analyses with R (Springer Texts in Statistics)

About the Author\nSilvia Bozza is Associate Professor of Statistics at Ca' Foscari University of Venice (Italy), Department of Economics and Senior Researcher at the University of Lausanne (School of Criminal Justice). Her research interests are mainly focused on Bayesian modelling, decision theory and probabilistic graphical models with applications in forensic science. 
Franco Taroni is Full Professor of Forensic Statistics at the Faculty of Law, Criminal Justice and Public Administration, School of Criminal Justice, of the University of Lausanne (Switzerland). He publishes extensively in the area of probabilistic reasoning, decision making and data analysis in forensic science. 
Alex Biedermann is Associate Professor at the Faculty of Law, Criminal Justice and Public Administration, School of Criminal Justice, of the University of Lausanne (Switzerland). He researches and teaches in the area of evidential reasoning and decision making at the intersection between forensic science and the law. His work is multidisciplinary and pertains to forensic science, law and topics in probability and decision theory.\nBayes 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.\nOver 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.\nFrom the Back Cover\nBayes 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

Computers & Technology

Software

Springer

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: <ul> <li>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.</li> </ul> <ul> <li>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.</li> </ul> <ul> <li>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.</li> </ul> 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 statisticiansinterested 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.

See the best price to sell, buy, or rent books by Biedermann, Alex. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Biedermann, Alex

See the best price to sell, buy, or rent books by Biedermann, Alex. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Bayesian Networks and Probabilistic Inference in Forensic Science

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science (Statistics in Practice)

Data Analysis in Forensic Science: A Bayesian Decision Perspective

Bayes Factors for Forensic Decision Analyses with R (Springer Texts in Statistics)

Bayes Factors for Forensic Decision Analyses with R (Springer Texts in Statistics)