Both LDA with collapsed sampling and SBTDM share an advantage in space Algorithm 1 Compute the sampling distribution for a product of two multinomials with SBT priors with Q(z) = 1 for all child c non-zero for ar and a l do ic ← INTERSECT(ar.c, a l .c) i.children += ic τ (i) += τ (ic) end for return i end function complexity: the model parameters are specified by a sparse set of non-zero counts denoting how often tokens of each word or document are assigned to each topic.
in general the sampling distribution for SBTDM has non-uniform probabilities for each of L different latent variable values.