Multiplicative Factorization of Noisy-Max

Abstract

The noisy-or and its generalization noisy-max have been utilized to reduce the complexity of knowledge acquisition. In this paper, we present a new representation of noisy-max that allows for efficient inference in general Bayesian networks. Empirical studies show that our method is capable of computing queries in well-known large medical networks, QMR-DT and CPCS, for which no previous exact inference method has been shown to perform well.

Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999)

References in corpus (1)

• Inference with Causal Independence in the CPSC Network

Cited by in corpus (2)

• Real-Time Inference with Large-Scale Temporal Bayes Nets
• Exploiting Functional Dependence in Bayesian Network Inference