emcee MCMC skill for Bayesian parameter estimation and posterior sampling in physics applications
--- name: emcee-mcmc-sampler-3826df6106f3 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--- name: emcee-mcmc-sampler-3826df6106f3 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
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.