Transition Analysis (TA) (Boldsen et al. 2002) statistically combines information from components of the cranial sutures, pubic symphyses, and iliac auricular surfaces to produce maximum likelihood point estimates and associated prediction intervals.

 

Rather than using multidimensional stages, the traits of each anatomical unit are scored as independent components. This TA approach better captures biological variability and uses the age-related variation present in each feature to produce probabilistically tailored maximum likelihood age estimates and associated prediction intervals.

 

To produce age estimates, data are entered into the TA program and the user enters estimated or known information about the skeleton (e.g., sex, ancestry). Trait data are combined with prior information about the population of interest to produce an individualized age estimates based on the traits observable and their character states. The method can be used with partial skeletons (e.g., if the cranium is missing) or if remains are damaged.