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name: modeling-primitives description: "Use ZhuSuan's distributions, BayesianNet, MetaBayesianNet, and stochastic tensor primitives to build and inspect probabilistic models." metadata: disco-role: operating disable-model-invocation: true license: MIT

Modeling Primitives

Use this sub-skill when the task is about ZhuSuan's core probabilistic-modeling surface rather than a specific inference algorithm.

Read this when

• the user names BayesianNet, MetaBayesianNet, StochasticTensor, meta_bayesian_net, reuse_variables, cond_log_prob, log_joint, query, or get
• the task is about distributions, observed nodes, latent nodes, node naming, group_ndims, n_samples, or shape semantics
• the user wants to understand how to build a model graph before training or sampling
• the request is about compatibility or migration from the deprecated legacy wrappers

What this sub-skill owns

• zhusuan.distributions and the BayesianNet.<distribution> helpers
• BayesianNet, StochasticTensor, MetaBayesianNet, and meta_bayesian_net
• observed-vs-latent node handling, deterministic nodes, and custom log_joint
• tensor-like behavior of stochastic nodes and shape checking rules
• safe use of get, query, cond_log_prob, and log_joint

What this sub-skill does not own

• ELBO/IWAE/VIMCO/RWS, importance sampling likelihoods, or flows
• HMC, SGMCMC, AIS, or sampling diagnostics
• dataset downloads, example training loops, or plotting utilities

Start here

1. Read references/modeling-concepts.md for the modeling rules and shape semantics.
2. Read references/api-reference.md when you need exact constructor or method signatures.
3. Read references/troubleshooting.md for dtype, shape, and deprecation issues.
4. Run scripts/core_smoke.py after environment setup to confirm the core package can build a tiny model and evaluate an ELBO.

Common tasks

Build a probabilistic model

• Create a BayesianNet.
• Add stochastic nodes with distribution helpers such as normal, bernoulli, categorical, dirichlet, or multivariate_normal_cholesky.
• Use deterministic TensorFlow ops for hidden calculations and optionally wrap them with bn.deterministic(...) when you want the node name tracked.

Reuse a model with different observations

• Wrap the model builder with @zs.meta_bayesian_net(scope=..., reuse_variables=True) when repeated observation patterns should share TensorFlow variables.
• Call model.observe(...) to instantiate a concrete BayesianNet with a specific observation map.

Inspect a network

• Use bn.get(...) to fetch nodes by name.
• Use bn.cond_log_prob(...) to inspect per-node conditional log probabilities.
• Use bn.log_joint() for the overall joint log probability.
• Use bn.query(..., outputs=True, local_log_prob=True) only when maintaining older code that still expects the deprecated tuple return.

Good entry points

• zhusuan/framework/bn.py
• zhusuan/framework/meta_bn.py
• zhusuan/distributions/base.py
• zhusuan/distributions/univariate.py
• zhusuan/distributions/multivariate.py
• scripts/core_smoke.py

Routing hints

• If the request is really about training a VAE/BNN or estimating posterior bounds, route to variational-inference.
• If the request is about HMC, SGHMC, SGNHT, or posterior sampling, route to mcmc-and-sampling.
• If the request is only about a generic TensorFlow model unrelated to probabilistic nodes or Bayesian inference, a different repo skill is likely a better fit.