Moment methods for decision analysis
Management Science
Uncertainty in Artificial Intelligence
Uncertainty in Artificial Intelligence
A linear approximation method for probabilistic inference
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Rhetorical relations for information retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Laplace's method, a family of asymptotic methods used to approximate integrals, is presented as a potential candidate for the tool box of techniques used for knowledge acquisition and probabilistic inference in belief networks with continuous variables. This technique approximates posterior moments and marginal posterior distributions with reasonable accuracy [errors are O(n-2) for posterior means] in many interesting cases. The method also seems promising for computing approximations for Bayes factors for use in the context of model selection, model uncertainty and mixtures of pdfs. The limitations, regularity conditions and computational difficulties for the implementation of Laplace's method are comparable to those associated with the methods of maximum likelihood and posterior mode analysis.