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Most successful Bayesian network (BN) ap­ plications to date have been built through knowledge elicitation from experts. This is difficult and time consuming, which has lead to recent interest in automated methods for learning BNs from data. We present a case study in the construction of a BN in an intel­ ligent tutoring application, specifically dec­ imal(More)
Decision support systems for weather forecasting have yet to tackle the key issues in formulating forecast policy, focussing instead on data presentation. Here we describe a method for eliciting forecasting information and outline a forecast ontology for codifying that information as a data management tool. The completed ontology will provide key(More)
A Bayesian Network ~BN! consists of a qualitative part representing the structural assumptions of the domain and a quantitative part, the parameters. To date, knowledge engineering support has focused on parameter elicitation, with little support for designing the graphical structure. Poor design choices in BN construction can impact the network’s(More)
Fog events occur at Melbourne Airport, Australia, approximately 12 times each year. Unforecast events are costly to the aviation industry, cause disruption and are a safety risk. Thus, there is a need to improve operational fog forecasting. However, fog events are difficult to forecast due to the complexity of the physical processes and the impact of local(More)
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