Stable local computation with conditional Gaussian distributions
Statistics and Computing
Learning Bayesian Belief Network Classifiers: Algorithms and System
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Local Propagation in Conditional Gaussian Bayesian Networks
The Journal of Machine Learning Research
Bayesian Networks and participatory modelling in water resource management
Environmental Modelling & Software
Environmental Modelling & Software
Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment
Environmental Modelling & Software
Environmental Modelling & Software
Ten steps applied to development and evaluation of process-based biogeochemical models of estuaries
Environmental Modelling & Software
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems
Mathematics and Computers in Simulation
Reforestation planning using Bayesian networks
Environmental Modelling & Software
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
Environmental Modelling & Software
Position Paper: Modelling with stakeholders
Environmental Modelling & Software
Adaptive modelling for adaptive water quality management in the Great Barrier Reef region, Australia
Environmental Modelling & Software
How to avoid a perfunctory sensitivity analysis
Environmental Modelling & Software
Hybrid Bayesian network classifiers: Application to species distribution models
Environmental Modelling & Software
An integrated approach to linking economic valuation and catchment modelling
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Environmental Modelling & Software
Prediction analysis of a wastewater treatment system using a Bayesian network
Environmental Modelling & Software
Environmental Modelling & Software
Model development of a Bayesian Belief Network for managing inundation events for wetland fish
Environmental Modelling & Software
The construction of causal networks to estimate coral bleaching intensity
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
A Multi-Objective Evolutionary Algorithm for enhancing Bayesian Networks hybrid-based modeling
Computers & Mathematics with Applications
Environmental Modelling & Software
Environmental Modelling & Software
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Bayesian networks (BNs) are increasingly being used to model environmental systems, in order to: integrate multiple issues and system components; utilise information from different sources; and handle missing data and uncertainty. BNs also have a modular architecture that facilitates iterative model development. For a model to be of value in generating and sharing knowledge or providing decision support, it must be built using good modelling practice. This paper provides guidelines to developing and evaluating Bayesian network models of environmental systems, and presents a case study habitat suitability model for juvenile Astacopsis gouldi, the giant freshwater crayfish of Tasmania. The guidelines entail clearly defining the model objectives and scope, and using a conceptual model of the system to form the structure of the BN, which should be parsimonious yet capture all key components and processes. After the states and conditional probabilities of all variables are defined, the BN should be assessed by a suite of quantitative and qualitative forms of model evaluation. All the assumptions, uncertainties, descriptions and reasoning for each node and linkage, data and information sources, and evaluation results must be clearly documented. Following these standards will enable the modelling process and the model itself to be transparent, credible and robust, within its given limitations.