Date of Award

Spring 5-30-2017

Document Type


Degree Name

Master of Science (MS)


Forensic Science



First Advisor

Nicholas Petraco

Second Reader

John Reffner

Third Advisor

Peter Shenkin


Statistical analysis of toolmarks using frequentist methods can be problematic for assorted reasons. Thus, in order to analyze toolmarks whilst avoiding these issues, a Bayesian approach is taken. Specifically for this thesis we discuss the computation of a specific Likelihood Ratio for toolmark comparisons. This Bayesian based approach involves using data already at hand in conjunction with a probability model in order to establish an estimate for its “value”, i.e. the “weight of evidence”. Making the calculations to obtain a Likelihood Ratio is very cumbersome and time consuming. Also many commercial software packages hide the process and underlying assumptions that go into its calculation. Using the open-source software called "R", we developed a package that, after the toolmark data is entered, a Likelihood Ratio for it is estimated. Ideally, using open-source software and having the code publicly available, collaboration on refining the overall estimation process can start and hopefully reach consensus.



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