Importance sampling algorithms for the propagation of probabilities in belief networks

This paper investigates the use of a class of importance sampling algorithms for probabilistic graphs in graphical structures. A general model for constructing importance sampling algorithms is given and then some particular cases are considered. Logical sampling and likelihood weighting are particular cases of the model. Our proposal will be an algorithm which uses the functions with less entropy (more informative) to simulate the variables and the functions with move entropy to weight the simulations In this way we expect to obtain more uniform weights. Some experimental tests are carried out comparing the performance of the proposed algorithms in randomly generated graphs.