A belief network approach to optimization and parameter estimation: application to resource and environmental management. Vans, O. Artificial Intelligence, 101(1-2):135-163, 5, 1998.
A belief network approach to optimization and parameter estimation: application to resource and environmental management [link]Website  abstract   bibtex   
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.
@article{
 title = {A belief network approach to optimization and parameter estimation: application to resource and environmental management},
 type = {article},
 year = {1998},
 identifiers = {[object Object]},
 keywords = {Bayesian methods,Belief networks,Environmental policies,Hybrid models,Optimization,Parameter estimation,Probabilistic models,Resource management,Water quality},
 pages = {135-163},
 volume = {101},
 websites = {http://www.sciencedirect.com/science/article/pii/S0004370298000101},
 month = {5},
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 abstract = {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.},
 bibtype = {article},
 author = {Vans, Olli},
 journal = {Artificial Intelligence},
 number = {1-2}
}

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