Applying stylometry to Authorship Attribution requires distilling the elements of an author's style sufficient to recognise their mark in anonymous documents. Often, this is accomplished by contrasting the frequency of selected features in the authors' works. A recent approach, CP2D, uses innovation processes to infer the author's identity, accounting for their propensity to introduce new elements. In this paper, we apply CP2D to a corpus of Classical Latin texts to test its effectiveness in a new context and explore the additional insight it can offer the scholar. We show its effectiveness on a corpus of classical Latin texts and how---moving beyond maximum likelihood---we can visualise the stylistic relationships and gather additional information on the relationships among documents.