Individual skill
pymc
Builds and checks Bayesian models with PyMC, including hierarchical models, NUTS MCMC, variational inference, mutable-data predictions, posterior predictive checks, diagnostics, and PSIS-LOO model comparison. Use for probabilistic modeling and uncertainty inference in PyMC.
skills/pymc/SKILL.md
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SKILL.mdcore · 10,736 bytesSKILL.mdcore · 10,736 bytesSKILL.mdcore · 10,736 bytesSKILL.mdcore · 10,736 bytesassets/hierarchical_model_template.pyruns · 5,794 bytesassets/linear_regression_template.pyruns · 4,740 bytesreferences/distributions.mddoc · 14,893 bytesreferences/model_patterns.mddoc · 4,627 bytesreferences/sampling_inference.mddoc · 8,044 bytesreferences/standard_workflow.mddoc · 4,889 bytesreferences/workflows.mddoc · 7,753 bytesscripts/model_comparison.pyruns · 14,760 bytesscripts/model_diagnostics.pyruns · 9,199 bytes
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# PyMC Bayesian Modeling
## Version and scope
Targets **PyMC 6.3.2**, **PyTensor 3.3.2**, and **ArviZ 1.3.0**. These are
local Python APIs, with no service endpoint, credentials, or remote inference
required. The native tests use the PyMC NUTS sampler on small synthetic models.
Optional nutpie/JAX samplers, SMC, and production-scale convergence are not
validated by those tests. Record the complete resolved environment, backend,
seeds, preprocessing, and data provenance for a scientific run.
Local macOS validation used `PYTENSOR_FLAGS="cxx="` after the C linker failed
with `library 'd64' not found`; this disables PyTensor C compilation for that
process. The Python library workflows passed under that setting; it does not