Describes probabilistic document retrieval systems as a sequential learning process, in which the system learns the parameters of probability distributions describing the frequencies of feature occurrences in relevant and nonrelevant documents. Several techniques for estimating the parameters of distributions are described and the results of tests of these systems are discussed. (27 references) (Author/CLB)