Hailfinder: Bayesian Network for Severe Weather Forecasting

Hailfinder Bayesian Network is a probabilistic model for forecasting severe weather. The network contains 56 nodes and 66 arcs, with 2656 parameters. It was authored by B. Abramson, J. Brown, W. Edwards, A. Murphy, and R. L. Winkler and published in 1996.

Use Cases

• Benchmarking Bayesian network inference algorithms based on the network's 56 nodes and 66 arcs
• Studying probabilistic reasoning for severe weather forecasting based on the model's parameters
• Teaching Bayesian network structure and parameterization based on the documented average Markov blanket size and degree

Strengths

• Network structure is explicitly defined with 56 nodes and 66 arcs
• Parameter count is documented as 2656
• Average Markov blanket size and degree are provided as 3.54 and 2.36 respectively

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

Provenance

Source

bnlearn Bayesian Network Repository