Specify Bayesian models in PyMC and fit them with MCMC or variational inference.
npx skills add https://github.com/shushuzn/Rairos --skill pymc-shushuzn
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/shushuzn/Rairos/tree/main/skills/pymc Command: npx skills add https://github.com/shushuzn/Rairos --skill pymc-shushuzn
This Skill helps you build Bayesian probabilistic models that quantify uncertainty, validate assumptions with predictive checks, and compare competing models with trustworthy diagnostics.
Use the pymc skill to build and validate a hierarchical Bayesian regression with prior predictive checks, NUTS sampling (with log_likelihood enabled), diagnostics, and a posterior predictive check for your dataset.
High-intent search queries and answers about installing and using this skill.
Build hierarchical Bayesian models in PyMC by defining probabilistic distributions with named dimensions and using non-centered parameterization to improve MCMC sampling stability for grouped data.
Diagnose MCMC sampling issues in Bayesian modeling by checking R-hat, effective sample size (ESS), and divergences to ensure reliable posterior inference and convergence.
Compare Bayesian models using LOO and WAIC by computing the log_likelihood for each model, then evaluating Pareto-k diagnostics to ensure the model comparison results are trustworthy.
PyMC supports variational inference through ADVI, providing fast approximations for probabilistic models as an alternative to MCMC sampling when computational efficiency is prioritized.
Validate Bayesian model assumptions with posterior predictive checks by simulating data from the fitted model distribution and comparing it against observed data to evaluate model fit.
Low effective sample size and divergences in Bayesian regression often indicate sampling issues; applying non-centered parameterization and checking prior choices can improve MCMC stability.
jaechang-hitsPerform Bayesian modeling workflows with PyMC and ArviZ.
xjtulycBuild Bayesian models with PyMC 5.x and ArviZ using NUTS sampling.
fuzzy-dynamicsBuild and infer Bayesian models with MCMC sampling and variational inference.
ZurybrGuide MCMC sampling with RStan or PyStan for Bayesian models.
