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name: mcmc-and-prediction description: "Run Pyro HMC/NUTS/MCMC diagnostics and prior/posterior predictive sampling workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MCMC And Prediction

Use this sub-skill when the user is running, tuning, or debugging Pyro Hamiltonian Monte Carlo workflows (NUTS, HMC, MCMC.run(), get_samples(), summary(), diagnostics()) or drawing prior/posterior predictive samples with Predictive, WeighedPredictive, or MHResampler.

Route First

• For NUTS/HMC setup, warmup/sample/chain choices, constrained-site initialization, transforms, diagnostics, and the eight-schools pattern, read references/mcmc-workflows.md.
• For prior predictive, posterior predictive from MCMC samples, deterministic and observed return sites, sample shapes, guide-based predictive, weighted predictive, and resampling, read references/prediction-workflows.md.
• For divergences, invalid initial parameters/support, max tree depth, multiprocessing chains, memory, discrete latent variables, progress bars, JIT, and predictive shape surprises, read references/troubleshooting.md.
• To confirm that a small self-contained CPU NUTS workflow runs in the active package environment, run scripts/eight_schools_mcmc_smoke.py. Start with python scripts/eight_schools_mcmc_smoke.py --help; use --disable-progbar in non-interactive logs.

Best-Fit Tasks

Use this sub-skill for requests like:

• "run NUTS/HMC on this Pyro model";
• "choose num_samples, warmup_steps, num_chains, or target_accept_prob";
• "initialize a positive/simplex/bounded latent site for MCMC";
• "read mcmc.summary() or mcmc.diagnostics() and fix divergences";
• "draw posterior predictive samples for observations or deterministic sites";
• "use Predictive with MCMC samples, an SVI guide, weighted samples, or MHResampler".

Boundaries And Reroutes

• Model primitives, pyro.sample, pyro.param, pyro.plate, observations, pyro.deterministic, seeds, and parameter-store basics: route to ../modeling-basics/.
• Distribution choice, support constraints, .to_event(), and detailed batch/event/plate shape algebra: route to ../distributions-and-shapes/.
• SVI training, ELBO choice, autoguides, optimizers, and guide fitting before guide-based predictive: route to ../svi-and-autoguides/.
• Handler ordering for condition, replay, trace, block, masking/scaling, config_enumerate, infer_discrete, and enumeration dimension allocation: route to ../effect-handlers-and-enumeration/.
• Long domain examples such as epidemiological HMC, neural transport HMC, or optional contrib integrations should be treated as reference patterns, not as required runtime scripts. Optional CUDA, funsor, Horovod, Lightning, Graphviz, torchvision, pandas, and scanpy support is unverified in the minimum runtime.

High-Value Checks Before Answering

1. Confirm the model has continuous latent state for HMC/NUTS; discrete latent sites must be observed, enumerated/marginalized correctly, or rerouted.
2. Keep validation on while debugging: use MCMC(..., disable_validation=False) and fix support/shape errors before tuning sampler parameters.
3. Use constrained values with init_strategy=init_to_value(...); use unconstrained tensors only when explicitly passing initial_params.
4. Inspect mcmc.diagnostics() for divergences, acceptance rate, n_eff, and r_hat; do not judge convergence from a tiny smoke run.
5. For prediction, decide whether each site should be conditioned from posterior_samples, sampled anew, or explicitly returned via return_sites.
6. For parallel=True predictive or multi-chain MCMC, ensure model functions are plate-annotated and multiprocessing-safe before blaming Pyro internals.