The sensitivity of the Bayesian HPD method to the choice of prior.

Statistical sampling error is present in every statistical calculations using DNA because all such statistics rely on a sample (database) of individuals, which is used to estimate the population frequencies of alleles. Curran et al.gave a method for estimating the sampling error of the statistics based on the region of the highest density of the Bayesian posterior (HPD). The Bayesian HPD method relies on the assumption of a prior distribution for the population allele frequencies as well as Monte Carlo simulation. In this paper we answer three pivotal questions. Firstly we address the question of how many Monte Carlo iterations are required to get sufficient accuracy in our estimates of sampling error. Secondly, we address the question of the appropriate choice of the prior distribution for the population allele frequencies. Thirdly, we demonstrate the flexibility of the Bayesian HPD over other methods.