2002 AnImportanceSamplingAlgorithmba

• (Yuan et al., 2002) ⇒ Changhe Yuan, and Marek J. Druzdzel. (2002). “An Importance Sampling Algorithm based on Evidence Pre-propagation.” In: Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence.

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Quotes

Abstract

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 using two techniques: loopy belief propagation [19, 25] and ε-cutoff heuristic [2]. We tested the performance of EPIS-BN on three large real Bayesian networks: ANDES [3], CPCS [21], and PATHFINDER[11]. We observed that on each of these networks the EPIS-BN algorithm outperforms AISBN [2], the current state of the art algorithm, while avoiding its costly learning stage.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2002 AnImportanceSamplingAlgorithmba	Changhe Yuan Marek J. Druzdzel			An Importance Sampling Algorithm based on Evidence Pre-propagation