Fast Propagation Algorithms for Singly Connected Networks and their Applications to Information Retrieval

There are some problems in which the time required for evaluating a Bayesian network must not be long. This means that not all the propagation algorithms could be applied in these situations, and therefore an appropriate method must be chosen. A type of evaluation that fulfills this requirement is known generically as "anytime algorithm". They are able to return an approximate output at any moment, solution that could be enough for some problems. In this paper two methods framed in this approach are presented. These are modifications of the Pearl's exact propagation algorithm for singly connected networks, designed with the final aim of reducing the propagation effort in domain problems where the number of variables is very large and the domain knowledge could be represented using that topology. As a very suitable test bed for measuring their quality, the field of Information Retrieval was selected. Results of a detailed experimentation are presented as well, showing a good performance.