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Modeling Human Decomposition: A Bayesian Approach

NCJ Number
310025
Journal
Forensic Science International Volume: 367 Dated: February 2025 Pages: 112309
Author(s)
D. Hudson Smith; Noah Nisbet; Carl Ehrett; Cristina I. Tica; Madeline M. Atwell; Katherine E. Weisensee
Date Published
February 2025
Length
10 pages
Annotation

This study uses a Bayesian approach to model human decomposition.

Abstract

This research paper develops a generative probabilistic model for decomposing human remains based on the postmortem interval (PMI) and a wide range of environmental and individualistic variables. This model explicitly represents the effect of each variable, including PMI, on the appearance of each decomposition characteristic, allowing for direct interpretation of model effects and enabling the use of the model for PMI inference and optimal experimental design. In addition, the probabilistic nature of the model allows for the integration of expert knowledge in the form of prior distributions. The authors fit this model to a diverse set of 2529 cases from the GeoFOR dataset and demonstrate that the model accurately predicts 24 decomposition characteristics with an ROC AUC score of 0.85. Using Bayesian inference techniques, the researchers invert the decomposition model to predict PMI as a function of the observed decomposition characteristics and environmental and individualistic variables, producing an R-squared measure of 71 %. Finally, the authors demonstrate how to use the fitted model to design future experiments that maximize the expected amount of new information about the mechanisms of decomposition using the Expected Information Gain formalism. Environmental and individualistic variables affect the rate of human decomposition in complex ways. These effects complicate the estimation of the PMI based on observed decomposition characteristics. (Published Abstract Provided)