Prior probabilities

In this study the stability of the observed error rates of the homoscedastic discriminant function relative to the number of parameters in the model using simulated data from multivariate normal populations was investigated. Three models were considered, the four, six and eight variables models, each having four values of the separator function (δ ). Equal and unequal prior probabilities were considered for the different number of parameter and separator function configurations. The asymptotic performance of the models was considered using the cross validation error rate estimation procedure. Results indicate the six variable models as being more stable (displaying less variability in the estimated error rates) than the other models under consideration. Less deterioration was observed for the six-variable model specification as was evident in the other models and this was more pronounced for smaller values of δ .

This study investigated the performance of the heteroscedastic discriminant function under the non-optimal condition of unbalanced group representation in the populations. The asymptotic performance of the classification function with respect to increased Mahalanobis' distance (under this condition) was considered. Results obtained have shown that the misclassification of observations from the smaller group escalates when the sample size ratio 1: 2 is exceeded (for small sample sizes). Results also show more sensitivity to ...

Linear Discriminant Functions (LDF) or homoscedastic discriminant function (derived from the assumption of homoscedasticity of the variance-covariance matrix). In this study, estimation of the stability (determined as a measure of within sample variability) in the error rates of the different models under consideration is of interest. This is an overall indicator of the performance of the discriminant function.

use of prior odds for missing persons identifications Alex Biedermann*, Franco Taroni and Pierre Margot Prior probabilities represent a core element of the Bayesian probabilistic approach to relatedness testing. This letter opinions on the commentary Use of prior odds for missing persons identifications by Budowle et al., published recently in this journal. Contrary to Budowle et al., we argue that the concept of prior probabilities (i) is not endowed with the notion of objectivity, (ii) is not a case for computation, and (iii) does not require new guidelines edited by the forensic DNA community–as long as probability is properly considered as an expression of personal belief.