emcee MCMC Sampler

SkillDev tools

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

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the emcee MCMC Sampler skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/physics/skills/emcee-mcmc-sampler/SKILL.md and read by ahel’s review.

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

Model Setup: Define log-probability function
Initialization: Initialize walkers appropriately
Sampling: Run ensemble sampler
Convergence: Check autocorrelation and convergence
Analysis: Extract posterior distributions

Tools/Libraries

emcee
corner
arviz

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: emcee-mcmc-sampler
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter