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, focusing on Bayes factors supported by data sets with an operational perspective, practical relevance, and applicability. It covers probabilistic inference, decision-making, and operational relevance, offering a balanced approach and complete sample code in R.

Format: Hardback
Length: 187 pages
Publication date: 12 September 2022
Publisher: Springer International Publishing AG

Bayes Factors for Forensic Decision Analyses with R offers a comprehensive and accessible introduction to computational Bayesian statistics using the R programming language. With a primary focus on Bayes factors supported by data sets, this book adopts an operational perspective, emphasizing practical relevance and applicability while keeping theoretical and philosophical justifications to a minimum. It provides a balanced treatment of three interconnected topics:

Probabilistic Inference: This chapter relies on the core concept of Bayesian inferential statistics to assist practicing forensic scientists in the logical and balanced evaluation of the weight of evidence. It explores the principles of Bayesian inference, including the calculation of Bayes factors and their interpretation in the context of forensic decision-making.

Decision Making: This chapter delves into how Bayes factors are interpreted in practical applications to help address questions of decision analysis involving the use of forensic science in the law. It discusses the practical implications of Bayes factors in forensic decision-making processes, such as evidence evaluation, suspect identification, and courtroom testimony.

Operational Relevance: This chapter combines inference and decision-making, providing a comprehensive approach backed up with practical examples and complete sample code in R. It includes sensitivity analyses and discussions on how to interpret results in the context of forensic science. By integrating inference and decision-making, the book aims to equip readers with the skills and knowledge necessary to apply Bayesian statistics in real-world forensic decision-making scenarios.

Over the past decades, probabilistic methods have firmly established themselves as a reference approach for managing uncertainty in virtually all areas of science, including forensic science. The Bayes theorem serves as the fundamental logical tenet for assessing how new information, specifically scientific evidence, should 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 transitioning from the prior to the posterior distribution. Bayes factors play a crucial role in guiding the thinking of scientists about the value of scientific evidence and forming the basis of logical and balanced reporting practices. They serve as essential foundations for evidence-based decision-making in the field of forensic science.

In conclusion, Bayes Factors for Forensic Decision Analyses with R provides a valuable resource for practitioners and students in the field of forensic science. It offers a comprehensive introduction to computational Bayesian statistics, focusing on Bayes factors supported by data sets. Through its practical and applied approach, the book equips readers with the skills and knowledge necessary to apply Bayesian statistics in forensic decision-making, enhancing the accuracy and reliability of evidence evaluation and supporting evidence-based decision-making processes in the legal system.

Weight: 457g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031098383
Edition number: 1st ed. 2022