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pymc-probabilistic-programming

// PyMC for flexible Bayesian modeling

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updated:March 4, 2026
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SKILL.md Frontmatter
namepymc-probabilistic-programming
descriptionPyMC for flexible Bayesian modeling
allowed-toolsBash,Read,Write,Edit,Glob,Grep
metadata[object Object]

PyMC Probabilistic Programming

Purpose

Provides PyMC capabilities for flexible Bayesian modeling and probabilistic programming in Python.

Capabilities

  • Hierarchical model specification
  • Custom distributions
  • Gaussian processes
  • MCMC and variational inference
  • Model diagnostics
  • ArviZ integration for visualization

Usage Guidelines

  1. Model Building: Use PyMC context managers
  2. Custom Distributions: Define distributions when needed
  3. Hierarchical Models: Build proper hierarchical structures
  4. Visualization: Use ArviZ for diagnostic plots

Tools/Libraries

  • PyMC
  • ArviZ
  • Theano/PyTensor