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.
- Version
- 2.0
- License
- Apache License, Version 2.0
- Compatibility
- Requires Python 3.12+ with PyMC 6.3.2, PyTensor 3.3.2 and ArviZ 1.3-compatible dependencies; NumPy, pandas, Matplotlib, h5netcdf and h5py for bundled helpers/artifacts. Network access for installation only. Optional nutpie, NumPyro and BlackJAX samplers need separate dependencies.
Pinned to revision 68105dd992f1, so it is the text this page describes rather than whatever the author pushed since.
Pre-approved tools experimental
Experimental field. Support varies between clients, so this list is what the author declared, not what your client will enforce.
- Read
- Write
- Edit
- Bash
Files
- skills/pymc/SKILL.md
- skills/pymc/SKILL_CN.md
- skills/pymc/assets/hierarchical_model_template.py
- skills/pymc/assets/linear_regression_template.py
- skills/pymc/references/distributions.md
- skills/pymc/references/model_patterns.md
- skills/pymc/references/sampling_inference.md
- skills/pymc/references/standard_workflow.md
- skills/pymc/references/workflows.md
- skills/pymc/scripts/model_comparison.py
- skills/pymc/scripts/model_diagnostics.py
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