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Solving the inverse problem of post-mortem interval estimation using Bayesian Belief Networks.

Stephanie Giles1, David Errickson1, Karl Harrison1

  • 1Cranfield Forensic Institute, Cranfield University, Bedford Campus, MK43 0AL, United Kingdom.

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|December 12, 2022
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Summary
This summary is machine-generated.

Bayesian Belief Networks effectively estimate post-mortem intervals (PMI) by analyzing decomposition data. This study validates their predictive power across different regions, offering a probabilistic approach to forensic science.

Keywords:
Bayesian Belief NetworksDecompositionForensic taphonomyInverse problemPost-mortem intervalTaphonomic variables

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Area of Science:

  • Forensic Anthropology
  • Computational Biology
  • Taphonomy

Background:

  • Estimating post-mortem interval (PMI) is crucial in forensic investigations.
  • Human decomposition is influenced by numerous taphonomic variables.
  • Bayesian Belief Networks (BBNs) offer a probabilistic framework for complex inverse problems.

Purpose of the Study:

  • To cross-compare retrospective human decomposition data across three geographical regions.
  • To assess the influence of taphonomic variables on decomposition rate using Total Decomposition Score (TDS).
  • To develop and validate BBNs for accurate PMI estimation.

Main Methods:

  • Collected and analyzed decomposition data from three distinct geographical locations (US and UK).
  • Developed two BBN models for PMI estimation using training data.
  • Performed sensitivity analysis to identify key variables influencing TDS.
  • Validated BBN models using independent datasets.

Main Results:

  • BBN models demonstrated predictive power for PMI estimation.
  • Models accounted for unique combinations of taphonomic variables affecting decomposition.
  • Achieved high mean posterior probabilities: 86% (US) and 81% (UK) for the experimental hypothesis.
  • All cases showed at least 'moderate' support for PMI evidence.

Conclusions:

  • BBNs provide a logical, probabilistic solution for modeling human decomposition complexities.
  • This approach quantifies uncertainties in PMI estimation.
  • BBNs communicate PMI with an associated degree of confidence, enhancing predictive power for unknown cases.