emcee-mcmc-sampler

emcee MCMC skill for Bayesian parameter estimation and posterior sampling in physics applications

509 stars

Best use case

emcee-mcmc-sampler is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

emcee MCMC skill for Bayesian parameter estimation and posterior sampling in physics applications

Teams using emcee-mcmc-sampler should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/emcee-mcmc-sampler/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/physics/skills/emcee-mcmc-sampler/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/emcee-mcmc-sampler/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How emcee-mcmc-sampler Compares

Feature / Agent	emcee-mcmc-sampler	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

emcee MCMC skill for Bayesian parameter estimation and posterior sampling in physics applications

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# emcee MCMC Sampler

## Purpose

Provides expert guidance on emcee for Bayesian parameter estimation in physics, including ensemble sampling and convergence diagnostics.

## Capabilities

- Affine-invariant ensemble sampling
- Parallel tempering support
- Autocorrelation analysis
- Convergence diagnostics
- Prior/likelihood specification
- Chain visualization

## Usage Guidelines

1. **Model Setup**: Define log-probability function
2. **Initialization**: Initialize walkers appropriately
3. **Sampling**: Run ensemble sampler
4. **Convergence**: Check autocorrelation and convergence
5. **Analysis**: Extract posterior distributions

## Tools/Libraries

- emcee
- corner
- arviz

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