Operations Research
Management Science
Intelligent decision support systems
Decision Support Systems
Intelligent inference systems based on influence diagrams
Decision Support Systems
The Mathematica Book
Representing and Solving Decision Problems with Limited Information
Management Science
Using AI and games for decision support in command and control
Decision Support Systems
Explaining clinical decisions by extracting regularity patterns
Decision Support Systems
Solving linear-quadratic conditional Gaussian influence diagrams
International Journal of Approximate Reasoning
Inference in hybrid Bayesian networks with mixtures of truncated exponentials
International Journal of Approximate Reasoning
Mixtures of Gaussians and minimum relative entropy techniques for modeling continuous uncertainties
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Consistent weights for judgements matrices of the relative importance of alternatives
Operations Research Letters
Answering queries in hybrid Bayesian networks using importance sampling
Decision Support Systems
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A measure of efficiency for influence diagram models with continuous decision variables is presented in order to evaluate whether the additional computational complexity required by a more accurate model is justified. The efficiency measure is a multi-objective utility function that considers both the accuracy and complexity of the ID model. Accuracy is determined as the mean squared error between influence diagram decision rules and an analytical solution. Complexity is assessed by tracking the run time required to obtain the solution. The resulting efficiency score considers the preferences of an individual decision maker for accuracy and complexity. Three influence diagram models are compared using the efficiency measurement, and an iterative solution procedure is introduced to improve model performance.