An algorithm for deciding if a set of observed independencies has a causal explanation
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Editorial: Bayesian networks in water resource modelling and management
Environmental Modelling & Software
Environmental Modelling & Software
Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment
Environmental Modelling & Software
The Black Swan: The Impact of the Highly Improbable
The Black Swan: The Impact of the Highly Improbable
Environmental Modelling & Software
Environmental Modelling & Software
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Use of a Bayesian network for Red Listing under uncertainty
Environmental Modelling & Software
A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland
Environmental Modelling & Software
Adaptive modelling for adaptive water quality management in the Great Barrier Reef region, Australia
Environmental Modelling & Software
Nonuniform dynamic discretization in hybrid networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Modeling net ecosystem metabolism with an artificial neural network and Bayesian belief network
Environmental Modelling & Software
Assessing the likelihood of realizing idealized goals: The case of urban water strategies
Environmental Modelling & Software
Good practice in Bayesian network modelling
Environmental Modelling & Software
Prediction analysis of a wastewater treatment system using a Bayesian network
Environmental Modelling & Software
Automated Bayesian quality control of streaming rain gauge data
Environmental Modelling & Software
The construction of causal networks to estimate coral bleaching intensity
Environmental Modelling & Software
Environmental Modelling & Software
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We develop a Bayesian network (BN) model that describes estuarine chlorophyll dynamics in the upper section of the Neuse River Estuary in North Carolina, using automated constraint based structure learning algorithms. We examine the functionality and usefulness of the structure learning algorithms in building model topology with real-time data under different scenarios. Generated BN models are evaluated and a final model is selected. Model results indicate that although the effect of water temperature and river flow on chlorophyll dynamics has remained unchanged following the implementation of the nitrogen Total Maximum Daily Load (TMDL) program; the response of chlorophyll levels to nutrient concentrations has been altered. The results stress the importance of incorporating expert defined constraints and links in conjunction with the automated structure learning algorithms to generate more plausible structures and minimize the sensitivity of the learning algorithms. This hybrid approach towards structure learning allows for the incorporation of existing knowledge while limiting the scope of the learning algorithms to defining the links between environmental variables for which the expert has little or no information.