Bayes factor: A useful tool to quantitatively evaluate and compare performance of multiple stature estimation equations

When stature estimation of incomplete skeletal remains is necessary, researchers select an estimation equation which will produce the most accurate estimates. The purpose of this study is to propose that, given prior information of a target sample, the Bayes factor can be a useful tool to quantitatively evaluate and compare performance of multiple equations in this regard. This study also explores the best-performing equations to reconstruct statures of Korean War casualties with a demonstration of equation comparisons by the Bayes factor. Thirty-three sets of stature estimates were generated using different equations based on the osteometric data of the Korean War casualties. The distribution of each set was compared to that of the population (i.e., Korean servicemen during the Korean War) using the Bayes factors and posterior probabilities generated by the R codes in the LearnBayes package. A higher Bayes factor indicates a closer similarity between the two distributions under comparison. The equation with the highest Bayes factor in this study was Choi et al.'s (1997) humerus equation (bf = 9.84), followed by the femur equation of the same authors (bf = 5.3). The Bayesian approach has advantages over the traditional frequentist approach primarily based on the p-value. Particularly, the Bayes factor can provide practical interpretations on the models under comparison, which allows for a quantitative prioritization of different models. Researchers can obtain more accurate stature estimates of a target sample by using the equation of the highest Bayes factor. (C) 2020 Elsevier B.V. All rights reserved.