Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Parametric Structure of Probabilities in Bayesian Networks
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
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The paper presents an efficient method for simulating the tails of a target variable Z = h(X) which depends on a set of basic variables X = (X1,...,Xn). To this aim, variables Xi;i = 1,...., n are sequentially simulated in such a manner that Z = h(x1,..., xi-1, Xi,..., Xn) is guaranteed to be in the tail of Z. When this method is difficult to apply, an alternative method is proposed, which leads to a low rejection proportion of sample values, when compared with the Monte Carlo method. Both methods are shown to be very useful to perform a sensitivity analysis of Bayesian networks, when very large confidence intervals for the marginal/conditional probabilities are required, as in reliability or risk analysis. The methods are shown to behave best when all scores coincide. The required modifications for this to occur are discussed. The methods are illustrated with several examples and one example of application to a real case is used to illustrate the whole process.