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Discussion of ‘Environmental decision‐making using bayesian networks: Creating an environmental report card’

Discussion of ‘Environmental decision‐making using bayesian networks: Creating an environmental... 1IntroductionBayesian networks (BNs) are graphical models for reasoning under uncertainty, where variables are represented by nodes, and dependencies among variables by arcs. These direct connections between nodes are often causal connections. The application of BNs to ecology has been widely increasing during the last years . Applications include diagnostic reasoning, that is, establishing environment health and safety from monitoring activities, or again, predictive reasoning, that is, predicting the results of a management option. One of the main reasons for their increasing success is that BNs provide an intuitive graphical representation of complex ecosystem interactions that can be useful in management and science integration . This intuitive graphical representation also allows for a participatory approach in the decision‐making process construction. In fact, a relevant feature of BNs is the capability of combining expert knowledge with monitoring data.The authors do not introduce a BN for either modelling or decision‐making purposes; rather, they use this framework to aggregate different levels of information. At the top of the graph, nodes (root) represent several water and sediment quality measures as directly obtained from monitoring. By descending the graph structure, the following nodes represent coarsest aggregation indexes, up to the final (leaf) node representing the http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Stochastic Models in Business and Industry Wiley

Discussion of ‘Environmental decision‐making using bayesian networks: Creating an environmental report card’

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References (8)

Publisher
Wiley
Copyright
Copyright © 2017 John Wiley & Sons, Ltd.
ISSN
1524-1904
eISSN
1526-4025
DOI
10.1002/asmb.2236
Publisher site
See Article on Publisher Site

Abstract

1IntroductionBayesian networks (BNs) are graphical models for reasoning under uncertainty, where variables are represented by nodes, and dependencies among variables by arcs. These direct connections between nodes are often causal connections. The application of BNs to ecology has been widely increasing during the last years . Applications include diagnostic reasoning, that is, establishing environment health and safety from monitoring activities, or again, predictive reasoning, that is, predicting the results of a management option. One of the main reasons for their increasing success is that BNs provide an intuitive graphical representation of complex ecosystem interactions that can be useful in management and science integration . This intuitive graphical representation also allows for a participatory approach in the decision‐making process construction. In fact, a relevant feature of BNs is the capability of combining expert knowledge with monitoring data.The authors do not introduce a BN for either modelling or decision‐making purposes; rather, they use this framework to aggregate different levels of information. At the top of the graph, nodes (root) represent several water and sediment quality measures as directly obtained from monitoring. By descending the graph structure, the following nodes represent coarsest aggregation indexes, up to the final (leaf) node representing the

Journal

Applied Stochastic Models in Business and IndustryWiley

Published: Aug 1, 2017

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