Win95pts: A 76-Node Bayesian Network for Probabilistic Inference

OpenML hosts the Win95pts Bayesian network, a discrete probabilistic model with 76 nodes and 112 arcs. The network contains 574 parameters and is referenced in the bnlearn Bayesian Network Repository. Its license is designated as us-pd.

Use Cases

• Benchmarking structure learning algorithms based on the known 76-node, 112-arc graph.
• Evaluating probabilistic inference performance based on the 574 parameters.
• Studying network topology characteristics based on the reported average Markov blanket size of 5.92 and average degree of 2.95.

Strengths

• Well-defined graph structure with 76 nodes and 112 arcs.
• Known complexity metrics, including a maximum in-degree of 7 and 574 total parameters.

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

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Time Range

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Freshness

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Geography

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