Since the dynamic topic model (DTM) is a probabilistic model, word probabilities are never zero even for words that do not occurr in a time slice. But the DTM has a temporal smoothing parameter that influences the temporal continuity of topics. In the LdaSeqModel(), it's the chain_variance parameter. By increasing it, words that do not occurr in a time slice get lower probabilities, also in the toy model given above.