Insurance Bayesian Network: A Discrete Medium-Scale Probabilistic Model

Insurance_6 is a Bayesian network sample from the bnlearn repository, designed for modeling probabilistic relationships in an insurance context. The network contains 27 nodes, 52 arcs, and 1008 parameters, with an average Markov blanket size of 5.19. It was created by J. Binder, D. Koller, S. Russell, and K. Kanazawa, with a foundational paper published in Machine Learning in 1997.

Use Cases

• Benchmarking Bayesian network structure learning algorithms based on the known 27-node, 52-arc topology.
• Testing probabilistic inference and reasoning algorithms based on the network's 1008 parameters.
• Studying the properties of discrete, medium-scale networks based on metrics like average degree (3.85) and maximum in-degree (3).
• Educational demonstrations of Bayesian network concepts in a domain-specific (insurance) context.

Strengths

• Well-defined network structure with 27 nodes and 52 arcs, providing a concrete benchmark.
• Substantial number of parameters (1008) for a medium-scale discrete network.
• Clear academic provenance with a cited 1997 publication in Machine Learning.

Limitations

• Row count, column definitions, and sample data are unavailable, limiting suitability assessment.
• 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 category.

Collection Method

Likely a synthetic or expert-constructed probabilistic model for research purposes.

Time Range

null

Freshness

unknown

Geography

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