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name: bayesian-networks description: "Fit Bayesian networks, query marginals, intervene, evaluate predictions, plot structures, and run latent-variable EM." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Bayesian Networks

Use this sub-skill when the graph already exists and you want CPDs, inference, interventions, evaluation metrics, plotting, or latent-variable EM.

Route here when

• The request names BayesianNetwork, InferenceEngine, roc_auc, classification_report, plot_structure, display_plot_ipython, EMSingleLatentVariable, or BayesianNetworkClassifier.
• You need to fit CPDs on a known DAG.
• You need causal queries or do-calculus interventions.
• You need a Bayesian-network classifier or latent-variable estimation.

Route elsewhere when

• You still need to learn the DAG -> ../structure-learning/SKILL.md.
• You need to discretize inputs before fitting a BN -> ../discretization/SKILL.md.
• You need synthetic fixtures or dynamic toy data -> ../synthetic-data/SKILL.md.

Start fast

1. Read references/api-reference.md for the BN constructors and query methods.
2. Read references/workflows.md for fitting, inference, evaluation, plotting, and EM recipes.
3. Run ../../scripts/smoke_bayesian_network.py after environment changes or when a query path breaks.
4. Read references/troubleshooting.md for CPD, node-name, and intervention failures.