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emcee-mcmc-sampler

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

QUICK START

How to use this skill

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  1. Open your project in Codex.
  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
Prompt to paste
I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/domains/science/physics/skills/emcee-mcmc-sampler/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/emcee-mcmc-sampler/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

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