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Abstract This study proposes a novel method for the construction of efficient and convenient Bayesian networks BNs and influence diagrams regarding medical problems based on fuzzy rules. Citing Literature. Volume 32 , Issue 3 Special Issue: Recent advances on knowledge discovery and business intelligence June Pages Related Information.
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Using a Bayesian Network as an Expert System
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Philip Dawid , Steffen L. Lauritzen , and Robert G. Cowell More by David J. Spiegelhalter Search this author in:. We review recent developments in applying Bayesian probabilistic and statistical ideas to expert systems. Using a real, moderately complex, medical example we illustrate how qualitative and quantitative knowledge can be represented within a directed graphical model, generally known as a belief network in this context.
Exact probabilistic inference on individual cases is possible using a general propagation procedure. When data on a series of cases are available, Bayesian statistical techniques can be used for updating the original subjective quantitative inputs, and we present a set of diagnostics for identifying conflicts between the data and the prior specification. A model comparison procedure is explored, and a number of links made with mainstream statistical methods.
Details are given on the use of Dirichlet prior distributions for learning about parameters and the process of transforming the original graphical model to a junction tree as the basis for efficient computation.
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The applicability of the common surrounding rockmass classification methods in a deep buried tunnel[J]. Classification of surrounding rock based on Delphi method and ideal point method[J]. Chinese Journal of Geotechnical Engineering. Classification of engineering rock based on support vector machine[J].
Rock and Soil Mechanics, , 23 6 : —, in chinese.
A proposed validation framework for expert elicited Bayesian Networks | QUT ePrints
Chinese Journal of Rock Mechanics and Engineering, , 24 9 : —, in chinese. Catastrophe progression method for stability classification of underground engineering surrounding rock[J]. Visual evaluation of neural network on classification of surrounding rocks for underground engineering[J]. Enabling a powerful marine and offshore decision-support solution through Bayesian network technique. Risk Analysis ; 26 3 : — Preventative maintenance modelling, a Bayesian perspective. Journal of Quality in Maintenance Engineering ; 2 1 : 15— Bayesian networks.
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Bayesian Network Based Expert System for Tunnel Surrounding Rockmass Classification
Log In. Paper Titles. Research and Analysis of a Suspension Bridge p. Analysis of Separated Bridge for Lateral Connection p. Article Preview. Abstract: Traditional surrounding rockmass classification methods have disadvantages of relative narrow scope of application, most of the time the classification result needs some modifications by geological expert and field situation.