byjames-traina· 20 skills
This skill covers Bayesian estimation and inference in quantitative social science. Use when the user is specifying priors, running MCMC, diagnosing chain convergence, or reporting posterior summaries — including hierarchical models, Bayesian structural models, and small-sample settings where priors regularize. Triggers on "Bayesian estimation", "Bayesian inference", "MCMC", "Markov chain Monte Carlo", "Stan", "PyMC", "NumPyro", "prior", "posterior", "credible interval", "Bayesian structural", "Bayesian BLP", "Bayesian DSGE", "hierarchical model", "random effects Bayesian", "posterior predictive check", "Bayes factor", "prior predictive check", "NUTS", "HMC", "Hamiltonian Monte Carlo", "R-hat", "rhat", "effective sample size", "ESS", "Bayesian calibration", "posterior distribution", "prior elicitation", "weakly informative prior", "brms", "rstanarm", "cmdstanpy", "pymc", "arviz".
$npx -y skills add james-traina/compound-science --skill bayesian-estimationInstalls into the current project.
Run `npx skills use "https://github.com/james-traina/compound-science" --skill "james-traina/compound-science/bayesian-estimation"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/james-traina/compound-science" that are relevant to the current task. Run `npx skills add "https://github.com/james-traina/compound-science"` and select the relevant skills, then follow their instructions.