AFTE Store - A Comprehensive Statistical Analysis of Striated Tool Mark Examinations, Part 2: Comparing Known Matches and Known Non-Matches using Likelihood Ratios

A potential model for increasing the objectivity in the interpretation of toolmarks is explored using consecutively matching striae (CMS) and Bayesian inference. Given the nature of the data, standard statistical thinking suggests that Bayesian inference is likely to be the most powerful method of interpretation. The unavoidable paucity of data for high CMS runs for the known non-match condition is handled using a small advance in modelling. The resulting likelihood ratios show some, but incomplete separation between the known match and known non-match conditions. Although promising, the resulting incomplete separation between known match and known non-match is thought to represent limitations of the CMS summary of the complete pattern and limitations of the modelling used.