Title
Tail uncertainty analysis in complex systems
Abstract
The paper presents an efficient computational method for estimating the tails of a target variable Z which is related to other set of bounded variables X = (X-1,...,X-n) by an increasing (decreasing) relation Z = h(X-1,...,X-n). To this aim, variables X-i, i = 1,..., n are sequentially simulated in such a manner that Z = h(x(1),..., x(i-1), X-i,..., X-n) is guaranteed to be in the tail of Z. The method is shown to be very useful to perform an uncertainty analysis of Bayesian networks, when very large confidence intervals for the marginal/conditional probabilities are required, as in reliability or risk analysis. The method is shown to behave best when all scores coincide and is illustrated with several examples, including two examples of application to real cases. A comparison with the fast probability integration method, the best known method to date for solving this problem, shows that it gives better approximations. (C) 1997 Elsevier Science B.V.
Year
DOI
Venue
1997
10.1016/S0004-3702(97)00052-0
Artif. Intell.
Keywords
Field
DocType
tail uncertainty analysis,complex system,monotonic transformation,conditional probability,uncertainty analysis,bayesian network,risk analysis,confidence interval
Complex system,Monotonic function,Discrete mathematics,Conditional probability,Uncertainty analysis,Bayesian network,Confidence interval,Fast Probability Integration,Mathematics,Bounded function
Journal
Volume
Issue
ISSN
96
2
0004-3702
Citations 
PageRank 
References 
4
0.72
5
Authors
3
Name
Order
Citations
PageRank
Enrique Castillo155559.86
Cristina Solares2467.89
Patricia Gómez382.15