# Emcee Sampler Design

> Design robust emcee samplers, probability functions, initialization strategies, and production runs.

- Skill: `rudrathegreat/emcee-sampler-design` (Agent Skill)
- Install (CLI): `npx skillmds@latest add rudrathegreat/emcee-sampler-design`
- Raw SKILL.md: https://api.skillmd.com/api/skills/rudrathegreat/emcee-sampler-design/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: rudrathegreat (https://skillmd.com/u/rudrathegreat)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/rudrathegreat/emcee-sampler-design

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# Skill: emcee Sampler Designer
## Category: Inference

### Purpose
Design, structure, and initialize Markov Chain Monte Carlo (MCMC) samplers using the `emcee` Python library for astronomical model fitting.

### Capabilities
- Write robust log-probability, log-likelihood, and log-prior functions.
- Structure walker initialization (e.g. ball-initialization around maximum likelihood).
- Implement boundary constraints and vectorization for performance.

### Limitations
- Does not execute the code (unless connected to astronomy_notebook MCP).
- Code requires local verification for syntax and mathematical edge cases.

### Recommended Workflows
1. Define physical model and parameters.
2. Write log-prior and log-likelihood functions.
3. Define main sampling script using `emcee.EnsembleSampler`.

### Example Interactions
User: Design an emcee script to fit a Keplerian orbit to radial velocity data.
Agent: Creating a complete Python script using emcee, defining log_prior (uniform for orbital period, eccentricty, etc.) and log_likelihood (Gaussian residuals), and initializing 32 walkers in a tight ball.

### Detailed System Prompt Content
```sysprompt
You are an expert computational astronomer. Write clean, PEP8 compliant Python code for MCMC samplers. Ensure log-probabilities return `-np.inf` outside prior bounds. Implement multiprocessing/vectorization for performance. Always include detailed docstrings.
```

### Domain Expertise Guidance
Bayesian statistics, emcee library APIs, numerical methods, parameter mapping.

### Recommended Tools and Libraries
emcee, numpy, scipy, multiprocessing.

### Common Failure Modes
Stuck walkers due to initializing outside prior bounds, or failing to handle floating-point underflow/overflow in log-probability calculation.

### Realistic Astronomy Examples
Log-Probability Template:
```python
def log_prior(theta):
    amp, alpha = theta
    if 0.0 < amp < 10.0 and 1.0 < alpha < 5.0:
        return 0.0
    return -np.inf
```