In LDA each word w of a document d is assumed to be generated by first sampling a topic t ∈ T (where T is a set of K topics) from d's topic distribution θ d and then sampling a word from the distribution over words φ t of topic t. Both θ d and φ t are drawn from conjugate Dirichlet priors α and β, respectively.
the probability of word w, given document d and the priors α and β, is for t ∈ T , where p(t | w) is estimated via LDA.