Build, fit, and validate Bayesian models with PyMC and ArviZ.
npx skills add https://github.com/HaykTarkhanyan/dst_research --skill pymc-hayktarkhanyan
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/HaykTarkhanyan/dst_research/tree/main/.claude/skills/pymc Command: npx skills add https://github.com/HaykTarkhanyan/dst_research --skill pymc-hayktarkhanyan
Bayesian modeling and probabilistic programming requires careful workflow to build, fit, and validate models with quantified uncertainty. This skill provides a practical, end-to-end blueprint for employing PyMC to design hierarchical models, perform MCMC or variational inference, and evaluate models with robust posterior checks.
Define your data, build a PyMC model, run MCMC or VI, and review diagnostics to interpret the posterior results.
High-intent search queries and answers about installing and using this skill.
Build Bayesian models with PyMC by defining data, priors, and likelihood, then validate using MCMC sampling and ArviZ diagnostics like R-hat, ESS, and divergences for accurate posterior results.
Compare Bayesian models using LOO and WAIC by computing information criteria with ArviZ, enabling model selection and model-averaging strategies to evaluate predictive accuracy across disciplines.
MCMC sampling with NUTS works by exploring posterior distributions in probabilistic programming, generating samples that quantify uncertainty for Bayesian models including regression, time-series, and hierarchical structures.
Use variational inference as an alternative to MCMC for Bayesian regression when faster approximation is needed, trading exact posterior exploration for computational efficiency in PyMC workflows.
ArviZ is required to perform posterior predictive checks, providing diagnostic plots and statistical summaries to validate model fit and assess whether simulated data matches observed distributions.
Divergences in MCMC sampling diagnostics indicate problematic posterior geometry, often caused by inappropriate priors or hierarchical model parameterization, requiring model reparameterization or prior adjustment.
jaechang-hitsPerform Bayesian modeling workflows with PyMC and ArviZ.
fuzzy-dynamicsBuild and infer Bayesian models with MCMC sampling and variational inference.
felixboehmBuild Bayesian models with PyMC and perform MCMC sampling.
galeepBuild and analyze Bayesian models with PyMC for MCMC and variational inference.
