Iowa State University
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Code for Hierarchical Bayesian Non-response Models for Error Rates in Forensic Black-Box Studies

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posted on 2023-04-04, 12:37 authored by Kori KhanKori Khan

This dataset contains all R code used to produce the results in the paper Khan, K., & Carriquiry, A. (2023). Hierarchical Bayesian non-response models for error rates in forensic black-box studies. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 381(2247), 20220157. 

In particular, it includes the JAGS models and run code for the naive, ignorable, and non-ignorable models discussed in the above-referenced paper. The naive and ignorable modes are fully Bayesian, but the non-ignorable is fit using an empirical Bayes approach. The code used to obtain the MLEs for the associated hyper-parameters in this model is also included. Finally, code to estimate error rates using the relevant posterior predective distribution is here, as well. 


This work was partially funded by the Center for Statistics and Applications in Forensic Evidence (CSAFE) through Cooperative Agreement 70NANB15H176 between NIST and Iowa State University, which includes activities carried out at Carnegie Mellon University, University of California Irvine, and University of Virginia.