  After a brief introduction to Probability Bracket Notation (PBN) for discrete
random variables in time-independent probability spaces, we apply both PBN and
Dirac notation to investigate probabilistic modeling for information retrieval
(IR). We derive the expressions of relevance of document to query (RDQ) for
various probabilistic models, induced by Term Vector Space (TVS) and by Concept
Fock Space (CFS). The inference network model (INM) formula is symmetric and
can be used to evaluate relevance of document to document (RDD); the
CFS-induced models contain ingredients of all three classical IR models. The
relevance formulas are tested and compared on different scenarios against a
famous textbook example.
