On Topic Evolution
I introduce topic evolution models for longitudinal epochs of word documents. The models employ marginally dependent latent state-space models for evolving topic proportion distributions and topicspecific word distributions; and either a logistic-normal-multinomial or a logistic-normal-Poisson model for document likelihood. These models allow posterior inference of latent topic themes over time, and topical clustering of longitudinal document epochs. I derive a variational inference algorithm for nonconjugate generalized linear models based on truncated Taylor approximation, and I also outline formulae for parameter estimation based on variational EM principle.