Win95pts: A 76-Node Bayesian Network for Probabilistic Inference

A Bayesian network with 76 nodes and 112 arcs, representing a discrete probabilistic model. The network contains 574 parameters and has an average Markov blanket size of 5.92. It is a sample from the bnlearn Bayesian Network Repository, a known source for discrete graphical models.

Use Cases

• Benchmarking structure learning algorithms based on the known network topology of 76 nodes and 112 arcs.
• Testing probabilistic inference methods based on the model's 574 parameters.
• Evaluating the efficiency of inference algorithms based on the average Markov blanket size of 5.92.
• Studying graphical model properties based on the reported average degree of 2.95 and maximum in-degree of 7.

Strengths

• Well-defined network structure with 76 nodes and 112 arcs, providing a concrete test case.
• Model complexity is quantified with 574 parameters and an average Markov blanket size of 5.92.
• Sourced from the bnlearn Bayesian Network Repository, a recognized collection for discrete models.

Limitations

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

Provenance

Source

bnlearn Bayesian Network Repository

Collection Method

Likely a constructed or learned graphical model for benchmark purposes.

Time Range

null

Freshness

Last updated date is unknown.

Geography

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