Generally, Relevance Model (RM) (Huang and Croft, 2009) refers to the probability distribution over all words conditioned on their occurrences in a set of previously-known relevant documents (or high-quality pseudo-relevant documents), i.e., , where V is the vocabulary, R is the document set, and P(w|R) can be estimated by TF-IDF.
the distribution is produced by densities within all interval ranges of relevance score.