Backward Simulation in Bayesian Networks
This addresses the challenge of efficient inference in Bayesian networks for practitioners, though it appears incremental as it builds on existing simulation methods.
The paper tackles the problem of approximate inference in Bayesian belief networks by introducing backward simulation, which starts from known evidence and works backward, improving convergence when posterior beliefs are dominated by evidence rather than prior probabilities, making it practical for many real-world applications.
Backward simulation is an approximate inference technique for Bayesian belief networks. It differs from existing simulation methods in that it starts simulation from the known evidence and works backward (i.e., contrary to the direction of the arcs). The technique's focus on the evidence leads to improved convergence in situations where the posterior beliefs are dominated by the evidence rather than by the prior probabilities. Since this class of situations is large, the technique may make practical the application of approximate inference in Bayesian belief networks to many real-world problems.