pymc-modeling
CommunityMaster Bayesian modeling with PyMC 5+.
Authorfonnesbeck
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Claude needs a structured, end-to-end guide for building, validating, and comparing Bayesian models in PyMC v5+, integrating priors, inference, diagnostics, and model testing into a cohesive workflow.
Core Features & Use Cases
- Comprehensive coverage of PyMC v5+ workflows: model specification, coordinates/dims, priors, sampling backends (nutpie, NUTS, NumPyro/JAX), ArviZ diagnostics, and model comparison.
- Templates and patterns for hierarchical/multilevel models, GLMs, Gaussian processes, time-series, BART, and mixture models, with practical examples.
- Diagnostics-driven workflow guidance including convergence checks, PPCs, LOO/WAIC, and posterior predictive checks, plus troubleshooting references.
Quick Start
Load the pymc-modeling skill and begin with a guided, hands-on workflow that defines data, specifies priors, runs inference, and assesses convergence.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: pymc-modeling Download link: https://github.com/fonnesbeck/pymc-modeling/archive/main.zip#pymc-modeling Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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