Bayes Factors for Forensic Decision Analyses with R

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 applicabilitykeeping 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 informationscientific evidenceought 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.

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 This book is open access, which means that you have free and unlimited access

Auteur
Silvia 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 reasoningand 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.

Texte du rabat

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 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.

Contenu
Chapter 1: Introduction to the Bayes factor and decision analysis.- Chapter 2: Bayes factor for model choice.- Chapter 3: Bayes factor for evaluative purposes.- Chapter 4: Bayes factor for investigative purposes.