A method for managing evidential reasoning in a hierarchical hypothesis space
Artificial Intelligence
On evidential reasoning in a hierarchy of hypotheses
Artificial Intelligence
Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Operations Research
Decision theory in expert systems and artificial intelligence
International Journal of Approximate Reasoning
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic inference and influence diagrams
Operations Research
Readings in uncertain reasoning
Readings in uncertain reasoning
Computational Statistics & Data Analysis - Optimal design and analysis of experiments
Artificial Intelligence - Special issue on artificial intelligence in perspective
Categorical and probabilistic reasoning in medicine revisited
Artificial intelligence in perspective
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
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An approach to use Bayesian belief networks in optimization is presented, with an illustration on resource and environmental management. A belief network is constructed to work parallel to a deterministic model, and it is used to update conditional probabilities associated with different components of that model. The divergence between prior and posterior probability distributions at the model components is used as an indication on the inconsistency between model structure, parameter values, and other information used. An iteration scheme was developed to force prior and posterior distributions to become equal. This removes inconsistencies between different sources of information. The scheme can be used in different optimization tasks including parameter estimation and optimization between various policy options. Also multiobjective optimization is possible. The approach is illustrated with an example on cost-effective management of river water quality.