The conditional distribution for sampling a topic z x for a token x during sampling can be factorized into document-topic and topic-word levels: where ⊗ is element-wise multiplication.
we have the following inequality: and the KL-divergence term in Theorem 3 is simply the sum of the KL-divergences between the conditional and prior distributions on all levels.