Build Bayesian models and run posterior inference with PyMC.
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill pymc-jasrajtulsi
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/pymc Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill pymc-jasrajtulsi
This Skill removes the complexity of building, fitting, and validating Bayesian models by providing a guided workflow for probabilistic programming, inference, and model checking in PyMC.
Ask the Skill to help you build, fit, and validate a PyMC Bayesian model for your dataset, including diagnostics and predictions.
High-intent search queries and answers about installing and using this skill.
You build hierarchical Bayesian models in PyMC by defining linear, logistic, or count models with weakly informative priors and named dimensions, then validate results using MCMC sampling diagnostics and posterior predictive checks.
Comparing Bayesian models with LOO or WAIC involves evaluating alternative specifications, interpreting Pareto-k reliability scores, and selecting the model that provides the most robust fit for your dataset.
Yes, PyMC supports time-series analysis and predictive forecasting by fitting Bayesian models to temporal data, validating posterior predictive fit, and generating uncertainty-aware predictions for new datasets.
Divergences during MCMC sampling indicate issues with model specification or posterior geometry; you diagnose and address them using PyMC's built-in sampling diagnostics, prior checks, and posterior predictive evaluations.
Yes, you can use variational inference instead of NUTS in PyMC to approximate posterior distributions for faster computation, followed by ArviZ-based evaluation to validate model quality and sampling effectiveness.
Yes, ArviZ is required to evaluate posterior predictive fit, verify convergence, inspect effective sample size, and interpret sampling diagnostics for Bayesian models built and fitted in PyMC.
RamanEbrahimiConstructs and analyzes Bayesian models using PyMC.
ovachieverBuild, fit, and evaluate Bayesian models with PyMC using MCMC sampling.
silversteinBuild PyMC Bayesian models with priors, likelihoods, and posterior inference.
ZurybrGuide MCMC sampling with RStan or PyStan for Bayesian models.
