As for the topic distribution priors, symmetric priors are often used, with a default value of 0.01 for all the vector elements (yielding sparse word distributions, as indicated earlier), meaning that each topic is expected to assign high probabilities to only a few top words (Steyvers and Griffiths 2007).
to the topic distribution priors, Wallach, Mimno, and McCallum (2009) found in their experiments on LDA that using an asymmetric β β β (D) was of no benefit.