Emphasizes the role of Bayes factor guided reasoning as a necessary preliminary to coherent decision analysis
Presents computational details and interpretation of output, recommended in forensic science
Demonstrates how to tackle practical problems and discusses in detail, so readers can analyze their own data
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Part of the book series: Springer Texts in Statistics (STS)
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About this book
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.
- Bayes factor
- scientific evidence
- decision making
- forensic science
- uncertainty management
- probability theory
- decision analysis
- Bayesian modeling
- Bayesian statistics
- probabilistic inference
- Open Access
Authors and Affiliations
Department of Economics, Ca’ Foscari University of Venice, Venice, Italy
Faculty of Law, Criminal Justice and Public Administration, School of Criminal Justice, University of Lausanne, Lausanne-Dorigny, Switzerland
Franco Taroni, Alex Biedermann
About the authors
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.
Book Title: Bayes Factors for Forensic Decision Analyses with R
Authors: Silvia Bozza, Franco Taroni, Alex Biedermann
Series Title: Springer Texts in Statistics
Publisher: Springer Cham
Copyright Information: Springer Nature Switzerland AG 2022
License: CC BY
Hardcover ISBN: 978-3-031-09838-3Published: 01 November 2022
Softcover ISBN: 978-3-031-09841-3Published: 01 November 2023
eBook ISBN: 978-3-031-09839-0Published: 31 October 2022
Series ISSN: 1431-875X
Series E-ISSN: 2197-4136
Edition Number: 1
Number of Pages: XII, 187
Number of Illustrations: 17 b/w illustrations, 5 illustrations in colour