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name: graph-models description: "Guides pomegranate BayesianNetwork and FactorGraph workflows, including categorical graph construction, structure learning, factor-graph inference, masked missing-value prediction, and graph troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

graph-models

Use this sub-skill when

Use this sub-skill for directed categorical Bayesian networks, direct factor-graph inference, categorical structure learning, and discrete missing-value inference with torch.masked.MaskedTensor. It covers BayesianNetwork, FactorGraph, Categorical, ConditionalCategorical, and JointCategorical interactions.

Start here

Typical imports:

from pomegranate.distributions import Categorical, ConditionalCategorical, JointCategorical
from pomegranate.bayesian_network import BayesianNetwork
from pomegranate.factor_graph import FactorGraph

Read references/api-reference.md for constructors, edge semantics, structure learning, and inference examples. Run scripts/smoke_graph_models.py for a self-contained Monty Hall Bayesian-network check and a tiny factor-graph check.

Core workflow

1. Encode discrete variables as integer categories. Bayesian networks and factor graphs are categorical/discrete in this implementation.
2. Choose manual structure or learned structure. For known graphs, pass distributions and edges. For data-driven categorical structure learning, pass structure or algorithm='chow-liu'/'exact' and call fit.
3. Use the right distribution type. Root Bayesian-network nodes use Categorical; child nodes with parents use ConditionalCategorical; factor-graph factors use Categorical or JointCategorical.
4. Run inference with masks. predict, predict_proba, and predict_log_proba infer missing variables from torch.masked.MaskedTensor where mask=True means observed.
5. Interpret approximate inference carefully. Factor-graph/sum-product inference is exact for tree-like structures and approximate for loopy graphs.

Route elsewhere when

• The task is ordinary distribution fitting or scoring: read ../distributions/SKILL.md.
• The task is a supervised Bayes classifier, not a graphical model: read ../mixtures-and-classifiers/SKILL.md.
• The task is a Markov chain or HMM sequence model: read ../sequence-models/SKILL.md.

Guardrails

• Edges are distribution-object pairs, not string node names.
• Do not use legacy State, Node, or bake; v1.x uses direct distribution objects.
• BayesianNetwork currently expects Categorical and ConditionalCategorical node distributions.
• FactorGraph is bipartite: edges connect marginals to factors, not factor-to-factor or marginal-to-marginal.
• predict_proba returns a list of tensors, one per variable, because variables can have different category counts.
• Read references/troubleshooting.md when edges, masks, category shapes, structure learning, or convergence fail.