Start Date
1-7-2010 12:00 AM
Abstract
Bayesian networks (BNs) are used increasingly to model environmental systems, for reasons including their ability to: integrate multiple issues and system components; utilise information from different sources; and handle missing data and uncertainty. 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 such guidelines to developing and evaluating Bayesian network models of environmental systems. 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 evaluated by sensitivity analysis, expert review and testing with cases. 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 minimum standards will help ensure the modelling process and the model itself is transparent, credible and robust, within its given limitations.
Guidelines for Good Practice in Bayesian Network Modelling
Bayesian networks (BNs) are used increasingly to model environmental systems, for reasons including their ability to: integrate multiple issues and system components; utilise information from different sources; and handle missing data and uncertainty. 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 such guidelines to developing and evaluating Bayesian network models of environmental systems. 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 evaluated by sensitivity analysis, expert review and testing with cases. 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 minimum standards will help ensure the modelling process and the model itself is transparent, credible and robust, within its given limitations.
