A method is proposed for the calibration of a continuous random variable when the dependent variables are a combination of continuous and categorical, and the model between the controlling variables and calibrated variable is empirically derived. The various probability distributions are estimated from training data by using kernel density procedures with bi-variate normal kernels for continuous variables and uniform smoothing for discrete variables. Bayes's theorem is then used to produce the posterior distribution from which point estimates and estimates of confidence may be made. Individual posterior densities allow each case to be considered separately and cases with conflicting evidence can easily be identified for further investigation. This approach is illustrated by using part of a data set of human adult teeth from individuals of known age. Estimates from the method proposed show less bias than those from the widely used multiple regression. This allows a more accurate reconstruction of the age distributions of ancient populations. In particular bias reduction is most notable at the extreme ages, which also tend to be the least frequent, thereby widening the age distribution. This will allow a more reliable consideration of archaeological and anthropological questions relating to, for example, the maximum lifespan, age-related social structure and the development of age-related disease.