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name: zhusuan description: "Use ZhuSuan for Bayesian networks, variational inference, HMC/SG-MCMC sampling, and importance-sampling workflows on TensorFlow 1.x." metadata: disco-role: operating disable-model-invocation: true license: MIT

ZhuSuan

ZhuSuan is a TensorFlow 1.x probabilistic-programming library for Bayesian deep learning. Use this skill when the user asks about import zhusuan as zs, Bayesian networks, latent-variable models, variational objectives, posterior sampling, or the repo's example workflows.

Quick start

1. Read references/overview.md for the module map and installation note.
2. Read references/workflows.md for the example-family map and dataset notes.
3. Read references/api-reference.md when you need exact signatures.
4. Read references/troubleshooting.md when install, TF1, dtype, or shape issues show up.
5. Run scripts/core_smoke.py after installing the package if you want a fast import-and-objective sanity check.

Route to a sub-skill

• sub-skills/modeling-primitives/SKILL.md for distributions, BayesianNet, MetaBayesianNet, observations, deterministic nodes, and node inspection.
• sub-skills/variational-inference/SKILL.md for ELBO, IWAE, KLPQ, importance-sampling likelihoods, normalizing flows, and VAE/BNN/SVGP-style training.
• sub-skills/mcmc-and-sampling/SKILL.md for HMC, SGLD, PSGLD, SGHMC, SGNHT, AIS, and chain diagnostics.

What this root skill is for

• Give the fastest route to the right workflow and reference.
• Provide the package-level install, import, and troubleshooting entry point.
• Keep the skill usable after the original checkout disappears.

What this root skill is not for

• It is not a full API manual; read the relevant reference file instead.
• It is not a training script collection; the example families are summarized in references/workflows.md and the sub-skills.
• It is not a replacement for the sub-skills; those carry the detailed workflow guidance.

Practical reminders

• ZhuSuan's core API is graph-based and TF1-style. Sessions, placeholders, and variable scopes still matter.
• The verified inspection environment used TensorFlow 1.15.5 with SciPy and mock on Python 3.6.
• The example families often need external data or optional image helpers, so keep them as references unless the user explicitly wants a full run.