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Babysitter pymc-probabilistic-programming

PyMC for flexible Bayesian modeling

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/mathematics/skills/pymc-probabilistic-programming" ~/.claude/skills/a5c-ai-babysitter-pymc-probabilistic-programming && rm -rf "$T"
manifest: library/specializations/domains/science/mathematics/skills/pymc-probabilistic-programming/SKILL.md
tags
#bayesian-modeling#probabilistic-programming#mcmc#arviz#statistical-modeling#custom-distributions
source content

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