Insurance Bayesian Network: A Discrete Medium-Scale Probabilistic Model

A Bayesian network with 27 nodes and 52 arcs, modeling insurance-related variables. The network contains 1008 parameters and was created by J. Binder, D. Koller, S. Russell, and K. Kanazawa, with a reference publication from 1997.

Use Cases

• Benchmarking Bayesian network structure learning algorithms based on the known 27-node, 52-arc topology.
• Testing probabilistic inference algorithms based on the network's 1008 parameters.
• Studying risk assessment models in insurance applications as suggested by the dataset's domain.
• Analyzing Markov blanket properties based on the reported average size of 5.19.

Strengths

• Well-defined network structure with 27 nodes and 52 arcs, providing a concrete benchmark.
• Detailed topological metrics provided, including average Markov blanket size (5.19) and average degree (3.85).
• Clear academic provenance with authors and a 1997 Machine Learning publication cited.

Limitations

• Row count, column names, and sample data are unknown, limiting suitability assessment for specific tasks.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

bnlearn Bayesian Network Repository (discrete-medium section), authors J. Binder, D. Koller, S. Russell, and K. Kanazawa.

Collection Method

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Time Range

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Freshness

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Geography

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