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:
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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.
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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.
