Insurance 8: A Bayesian Network for Insurance Risk Modeling

27 nodes and 52 arcs define this discrete Bayesian network for insurance applications. The network contains 1008 parameters and was created by J. Binder, D. Koller, S. Russell, and K. Kanazawa, with a reference publication from 1997. It is part of the bnlearn Bayesian Network Repository's discrete-medium collection.

Use Cases

• Modeling insurance risk factors based on the described Bayesian network structure
• Testing probabilistic inference algorithms on a known, medium-sized network
• Benchmarking structure learning algorithms against the 27-node, 52-arc reference model
• Studying the relationship between hidden variables and observable factors in insurance contexts

Strengths

• Well-defined structure with 27 nodes and 52 arcs, providing a clear probabilistic model
• Part of a curated repository (bnlearn), suggesting peer validation
• Includes key network metrics like an average Markov blanket size of 5.19 and maximum in-degree of 3

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment for certain tasks
• Last update date is unknown; freshness unverified

Provenance

Source

bnlearn Bayesian Network Repository

Collection Method

Likely constructed as a synthetic or expert-defined model for research and benchmarking.

Time Range

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Freshness

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Geography

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