Bayes Factors for Forensic Decision Analyses with R

Details

Ressource 1Download: BozzaTaroniBiedermann_2022.pdf (2363.35 [Ko])
State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_2AB34119FC9E
Type
Book:A book with an explicit publisher.
Collection
Publications
Institution
Title
Bayes Factors for Forensic Decision Analyses with R
Author(s)
Bozza Silvia, Taroni Franco, Biedermann Alex
Publisher
Springer International Publishing
ISBN
9783031098383
9783031098390
ISSN
1431-875X
2197-4136
Publication state
Published
Issued date
2022
Peer-reviewed
Oui
Series
Springer Texts in Statistics
Language
english
Number of pages
187
Abstract
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.
Open Access
Yes
Funding(s)
Swiss National Science Foundation / 10BP12_208532
Create date
03/11/2022 13:18
Last modification date
04/11/2022 7:08
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