Bozza Silvia, Taroni F., Biedermann A. Bayes Factors for Forensic Decision Analyses With R

Springer, 2022. — 362 p. — (Springer Texts in Statistics). — ISBN 978-3-031-09839-0.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.Introduction to the Bayes Factor and Decision Analysis
Bayes Factor for Model Choice
Bayes Factor for Evaluative Purposes
Bayes Factor for Investigative Purposes