Specifically, when we used uninformed uniform priors on the document/author word split (δ (D) = δ (A) = 1), and the same wordin-topic priors for both document and author words ( = 0), the obtained accuracy was comparable to AT-P's accuracy.
setting δ (D) = 1.222 and δ (A) = 4.889, which encodes our prior belief that on average 80% (with a standard deviation of 15%) of each document is composed of author words, significantly improved performance.