Hailfinder: A Bayesian Network for Severe Weather Forecasting
by B. Abramson, J. Brown, W. Edwards, A. Murphy, and R. L. Winkler.
arff
Available on 1 platform
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Description
Hailfinder is a Bayesian network model for forecasting severe weather, developed by B. Abramson, J. Brown, W. Edwards, A. Murphy, and R. L. Winkler. The model contains 56 nodes and 66 arcs, representing 2656 parameters. It was published in the International Journal of Forecasting in 1996.
Use Cases
Benchmarking Bayesian network structure learning algorithms based on the known 56-node, 66-arc topology.
Testing probabilistic inference methods for weather forecasting based on the model's 2656 parameters.
Studying the application of graphical models to meteorological decision support systems.
Evaluating the computational efficiency of inference on networks with an average Markov blanket size of 3.54.
Strengths
Well-documented model structure with 56 nodes and 66 arcs.
Defined network complexity metrics, including 2656 parameters and a maximum in-degree of 4.
Clear academic provenance with a published citation from 1996.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Row count is unknown, which may limit suitability assessment.
Last update date is unknown; freshness unverified.