An Importance Sampling Algorithm Based on Evidence Pre-propagation
This addresses a specific bottleneck in Bayesian network inference for domains like medical diagnosis or risk assessment, offering a more efficient solution.
The paper tackled the problem of precision deterioration in stochastic sampling algorithms for Bayesian networks under extremely unlikely evidence, proposing the EPIS-BN algorithm which showed considerable improvement over the state-of-the-art AIS-BN algorithm on three large real networks.
Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the Evidence Pre-propagation Importance Sampling algorithm (EPIS-BN), an importance sampling algorithm that computes an approximate importance function by the heuristic methods: loopy belief Propagation and e-cutoff. We tested the performance of e-cutoff on three large real Bayesian networks: ANDES, CPCS, and PATHFINDER. We observed that on each of these networks the EPIS-BN algorithm gives us a considerable improvement over the current state of the art algorithm, the AIS-BN algorithm. In addition, it avoids the costly learning stage of the AIS-BN algorithm.