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.
