emcee-mcmc-sampler

name	emcee-mcmc-sampler
description	emcee MCMC skill for Bayesian parameter estimation and posterior sampling in physics applications
allowed-tools	["Bash","Read","Write","Edit","Glob","Grep"]
metadata	{"specialization":"physics","domain":"science","category":"data-analysis","phase":6}
graph	{"domains":["domain:physics"],"skillAreas":["skill-area:statistical-analysis","skill-area:mathematical-reasoning","skill-area:data-analysis"],"workflows":["workflow:experiment-design","workflow:peer-review-cycle"],"roles":["role:research-scientist","role:computational-scientist"]}

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