Build Bayesian probabilistic models in PyMC with MCMC sampling and convergence diagnostics.
npx skills add https://github.com/gabrielvuksani/wotann --skill pymc-gabrielvuksani
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/gabrielvuksani/wotann/tree/main/skills/scientific/pymc Command: npx skills add https://github.com/gabrielvuksani/wotann --skill pymc-gabrielvuksani
This Skill helps you build Bayesian probabilistic models that produce credible uncertainty estimates instead of single-point answers, and then validate those models with diagnostics and predictive checks.
Fit a Bayesian model using NUTS on your dataset, generate posterior predictive samples, and run diagnostics to confirm convergence.
High-intent search queries and answers about installing and using this skill.
You build hierarchical Bayesian models in PyMC by defining priors, likelihoods, and named dimensions for interpretable inference. This Skill provides templates for hierarchical workflows that cover the full Bayesian loop end-to-end.
MCMC sampling with NUTS runs full posterior inference, validated by checking convergence diagnostics like R-hat, ESS, and divergences. This Skill guides you through sampling pathologies and log_likelihood support.
The best way to compare Bayesian models is using information criteria like LOO and WAIC via log-likelihood support. This Skill provides utilities for ranking fitted models and checking LOO reliability using Pareto-k values.
PyMC supports prior and posterior predictive checks to validate probabilistic models and quantify uncertainty from data. This Skill satisfies workflow requirements by integrating predictive checks with MCMC sampling and convergence diagnostics.
PyMC fits common Bayesian model patterns including linear regression, logistic regression, and Poisson or negative binomial count models. This Skill also covers time series, AR models, and missing-data handling.
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viniruggeriBuild, fit, and compare Bayesian models with PyMC and ArviZ.
ZurybrGuide MCMC sampling with RStan or PyStan for Bayesian models.
