  In this work, we employ quantitative methods from the realm of statistics and
machine learning to develop novel methodologies for author attribution and
textual analysis. In particular, we develop techniques and software suitable
for applications to Classical study, and we illustrate the efficacy of our
approach in several interesting open questions in the field. We apply our
numerical analysis techniques to questions of authorship attribution in the
case of the Greek tragedian Euripides, to instances of intertextuality and
influence in the poetry of the Roman statesman Seneca the Younger, and to cases
of "interpolated" text with respect to the histories of Livy.
