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A critical step in age-at-death estimation is to identify the age-at-death distribution to which the unknown skeletal remains most likely belong. Age estimation based on a frequentist approach assumes that the age distribution of the target population is same as that of the reference sample. In a Bayesian framework, researchers have greater flexibility, with the freedom to specify mortality information via a prior distribution and integrate it with osteological data to produce more accurate age estimates. The selection of an optimal prior remains challenging, as forensic anthropologists analyze unidentified individuals often originating from unknown populations. Understanding the effects of the prior choice on the final age produced for various populations is essential to the interpretation of these estimates.
In this article we investigate the effects of three different priors on age estimates using scores of three traditional methods collected on Asian skeletal samples representing peoples from Japan and Thailand. We test a uniform prior, which assumes the equal chance of death regardless of age, as well as various informative priors, derived from a Japanese mortality database and skeletal collections. We combine each of the priors with the parameters of the cumulative probit regression model to obtain age estimates. The results of our analyses show that the informative prior outperforms when it is carefully chosen to reflect the geographic and temporal origins of the target population. While the uniform prior produces the least-biased
age estimates, one should be cautious, as it can generate unrealistically old and inaccurate ages.