Win95pts: Bayesian Network for Windows 95 Performance Benchmarking

A Bayesian network model with 76 nodes and 112 arcs, representing performance and system interactions for Windows 95. The model contains 574 parameters and was sourced from the bnlearn Bayesian Network Repository. Its structure includes an average Markov blanket size of 5.92 and a maximum in-degree of 7.

Use Cases

• Probabilistic inference on system performance based on the network's 76 variables.
• Causal structure learning and analysis using the provided 112 arcs.
• Benchmarking Bayesian network algorithms against a known, discrete-large structure.
• Studying conditional dependencies in software systems via the model's parameters.

Strengths

• Well-defined network structure with 76 nodes and 112 arcs.
• Model complexity is quantified with 574 parameters and an average Markov blanket size of 5.92.
• Sourced from an authoritative repository (bnlearn) for discrete, large Bayesian networks.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and sample data are unavailable, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection.

Provenance

Source

bnlearn Bayesian Network Repository

Collection Method

Likely constructed to model performance and system interactions for Windows 95.

Time Range

null

Freshness

Last update date is unknown; freshness unverified.

Geography

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