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Babysitter stan-bayesian-modeling

Stan probabilistic programming for Bayesian inference

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 "$T/library/specializations/domains/science/mathematics/skills/stan-bayesian-modeling" ~/.claude/skills/a5c-ai-babysitter-stan-bayesian-modeling && rm -rf "$T"
manifest: library/specializations/domains/science/mathematics/skills/stan-bayesian-modeling/SKILL.md
tags
#bayesian-inference#mcmc-sampling#statistical-modeling#stan#probabilistic-programming
source content

Stan Bayesian Modeling

Purpose

Provides Stan probabilistic programming capabilities for Bayesian inference and statistical modeling.

Capabilities

  • Stan model specification
  • MCMC sampling (NUTS, HMC)
  • Variational inference
  • Prior predictive checks
  • Posterior predictive checks
  • Model comparison (LOO-CV, WAIC)

Usage Guidelines

  1. Model Specification: Write Stan code with clear blocks
  2. Prior Selection: Choose appropriate, weakly informative priors
  3. Diagnostics: Check Rhat, ESS, and divergences
  4. Model Comparison: Use LOO-CV for model selection

Tools/Libraries

  • Stan
  • CmdStan
  • RStan
  • PyStan