Win95pts: Bayesian Network for Computer System Performance

Win95pts is a Bayesian network with 76 nodes and 112 arcs, modeling relationships within a computer system. It contains 574 parameters and is sourced from the bnlearn Bayesian Network Repository. The dataset's specific temporal and geographic scope is not provided.

Use Cases

• Benchmarking Bayesian network structure learning algorithms based on the known network topology.
• Testing probabilistic inference algorithms based on the 574 parameters and average Markov blanket size of 5.92.
• Studying causal relationships in computer system performance based on the domain description.

Strengths

• Well-defined network structure with 76 nodes and 112 arcs.
• Known statistical properties, including 574 parameters and an average degree of 2.95.
• Sourced from an authoritative repository (bnlearn).

Limitations

• Row count and column definitions are unknown, limiting suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

bnlearn Bayesian Network Repository

Collection Method

Likely constructed as a model for a discrete system.

Time Range

null

Freshness

Last updated date is unknown.

Geography

null