Application of the Bayesian framework for forensic interpretation to casework involving postmortem interval estimates of decomposed human remains.

We demonstrate how the Bayesian framework for forensic interpretation can be adapted for casework involving postmortem intervals (PMI) utilizing taphonomic data as well as how to overcome some of the limitations of current approaches for estimating and communicating uncertainty. A model is implemented for indoor cases based on partial body scores from three different anatomical regions as correlated functions of accumulated temperature (AT). The multivariate model enables estimation of PMI for human remains also when one or two local body scores are missing or undetermined, e.g. as a result of burns, scars or covered body parts. The model was trained using the expectation maximization algorithm, enabling us to account for uncertainty of PMI and/or ambient temperature in the training data. Alternative approaches reporting the results are presented, including the likelihood curve, likelihood ratios for competing hypotheses and posterior probability distributions and credibility intervals for PMI. The applicability or the approaches in different forensic scenarios is discussed.