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name: modeling-and-factors description: "Builds and validates pgmpy graph, model, CPD, factor, and backend objects for causal and probabilistic graphical models." disable-model-invocation: true metadata: disco-role: operating license: MIT

Modeling and Factors

Use this sub-skill when the task is to construct, validate, inspect, or debug pgmpy graph/model/factor objects before learning, inference, simulation, causal effects, or file I/O.

Route here

• Build graph-only objects: DAG, PDAG, MAG, ADMG, SimpleCausalModel, and UndirectedGraph, including causal roles such as exposures, outcomes, latents, confounders, mediators, and instruments.
• Build probabilistic model containers: DiscreteBayesianNetwork, LinearGaussianBayesianNetwork, FunctionalBayesianNetwork, DynamicBayesianNetwork, DiscreteMarkovNetwork, FactorGraph, JunctionTree, ClusterGraph, and MarkovChain.
• Define and validate CPDs/factors: TabularCPD, LinearGaussianCPD, FunctionalCPD, DiscreteFactor, JointProbabilityDistribution, and NoisyORCPD.
• Diagnose add_cpds, get_cpds, get_factors, check_model, cardinality, state-name, evidence-order, graph-role, factor-scope, and backend-configuration errors.

Reroute elsewhere

• Parameter fitting, structure learning, CI tests, and scores: route to learning-structure-and-parameters.
• Posterior queries, MAP, simulation, and sampling beyond a tiny validation smoke: route to inference-sampling-and-simulation.
• Adjustment/frontdoor identification, do-calculus, ATE, IV, and effect-regressor workflows: route to causal-identification-and-effects.
• Model serialization formats, datasets/example models, graph metrics, and evaluation: route to data-io-and-evaluation.
• Adding or modifying pgmpy source APIs/templates: route to extending-pgmpy.

Read next

• Model and factor API reference for class-selection tables, CPD/factor shape rules, role methods, backend config, and validation semantics.
• Modeling workflows for self-contained recipes adapted from pgmpy's discrete BN, linear Gaussian BN, CPD, dynamic BN, functional BN, and factor-graph examples.
• Troubleshooting for check_model() failures, CPD evidence/cardinality mismatches, state-name confusion, optional torch/Pyro errors, and graph-only-vs-model decisions.
• Smoke script to verify that an installed pgmpy package can build and validate a tiny discrete BN from any working directory.

Minimal operating checklist

1. Choose the lightest object that matches the task: graph-only roles if no CPDs are needed; a BN/Markov/factor container if parameters are needed; a dynamic or functional model only when its extra structure is essential.
2. Add all nodes/edges before adding CPDs or factors. CPD/factor variables must already exist in the graph/model.
3. For TabularCPD, make values a 2-D array with shape (variable_card, product(evidence_card)); every column is a conditional distribution and must sum to 1 before check_model() passes.
4. Keep parent/evidence names, cardinalities, and state_names consistent across parent CPDs and child CPDs. Use the same labels in inference/evidence later.
5. Call model.check_model() before handing the model to learning, inference, simulation, causal, or I/O workflows.
6. Treat FunctionalBayesianNetwork and FunctionalCPD as optional: they require the torch backend plus pyro-ppl; they were not installed in the minimum CPU environment used for the core skill.

Safe validation command

From any directory with pgmpy installed, run:

python path/to/check_modeling_smoke.py

The script builds a tiny discrete Bayesian network, validates CPDs, runs one small exact query as a smoke check, and reports whether optional torch/Pyro packages are importable without requiring them.