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emcee-mcmc-sampler

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

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updated:March 4, 2026
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SKILL.md Frontmatter
nameemcee-mcmc-sampler
descriptionemcee MCMC skill for Bayesian parameter estimation and posterior sampling in physics applications
allowed-toolsBash,Read,Write,Edit,Glob,Grep
metadata[object Object]

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