Probabilistic Retrieval Model

We are interested in using the following document-term matrix and the associated relevance information as training data for a probabilistic retrieval model. A 1 entry indicates that the term occurs in a document, and 0 means it does not: R or NR indicate the relevance of the document with respect to queries in the training data.

Using the basic probabilistic retrieval model, compute the relevance and non-relevance probabilities associated with terms T1 through T6 (show these probabilities in a table). Then, using these probabilities and the given query Q = (1,1,0,1,0,1), compute the discriminant Disc(Q, D11) and Disc(Q, D12) for each of the two new documents:

D11 = (0,1,1,0,0,1)

D12 = (1,0,1,1,0,1)

Based on the discriminants, should these documents be retrieved? Explain your answer.