: Using Bayes factors to quantify the "weight of evidence" by measuring the change from prior to posterior odds.

: Discriminating between general propositions when no specific person or object of interest is available (e.g., general source characteristics).

: Introduction to Bayes' theorem as the standard for managing scientific uncertainty. Investigation vs. Evaluation :

: The text introduces MCMC (Markov Chain Monte Carlo), importance sampling, and Chib's formula for calculating Bayes factors.

: Moving beyond mere evaluation to coherent decision-making, helping scientists and legal professionals address practical questions under uncertainty.

: Providing real-world forensic examples and complete R sample code for sensitivity analyses and result interpretation. Key Concepts Covered

The book (published in 2022) provides a comprehensive introduction to using Bayesian methods—specifically Bayes factors —to evaluate scientific evidence and support rational decision-making in forensic science.

Authored by , the text focuses on practical application over abstract theory, utilizing the R programming language to demonstrate computational techniques. Core Themes The content is structured around three primary pillars: