Build and validate Bayesian models with PyMC 5+ and ArviZ.
npx skills add https://github.com/BKZhao/MIMIC-Papers-Repro-Agent --skill pymc-bkzhao
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/BKZhao/MIMIC-Papers-Repro-Agent/tree/main/.codex/skills/pymc Command: npx skills add https://github.com/BKZhao/MIMIC-Papers-Repro-Agent --skill pymc-bkzhao
PyMC enables building, calibrating, and validating Bayesian models with a modern Python API, streamlining uncertainty quantification across disciplines.
Run a simple PyMC model by defining data, specifying priors, sampling with pm.sample, and inspecting the idata with ArviZ.
High-intent search queries and answers about installing and using this skill.
Bayesian modeling in Python uses PyMC 5+ to define hierarchical structures, sample with NUTS, and validate uncertainty using ArviZ. This workflow covers linear, logistic, and time-series probabilistic programming with prior and posterior predictive checks.
Posterior predictive checks compare Bayesian models by evaluating fit and diagnostics through ArviZ. You sample models with PyMC, inspect the idata object, and run model comparison workflows to validate probabilistic assumptions rigorously.
PyMC supports time-series analysis and logistic regression by applying probabilistic programming to structured pandas and numpy data workflows. You specify priors, sample with pm.sample, and inspect inference data for rigorous insights.
ArviZ and PyMC 5+ are required to run MCMC sampling and variational inference. This Bayesian modeling stack provides the modern Python API needed for calibration, diagnostics, and validating uncertainty quantification across disciplines.
Bayesian modeling with PyMC is not ideal for workflows lacking structured data or requiring rapid point estimates without uncertainty quantification. It demands a structured data workflow and familiarity with prior specification, MCMC diagnostics, and posterior validation.
The pymc skill provides reusable templates and a curated reference library for common Bayesian workflows. These assets accelerate adoption for linear, logistic, hierarchical, and time-series analyses by streamlining data definition and prior specification.
ManfronEnricoBuild Bayesian models with PyMC for sampling, diagnostics, and posterior predictive checks.
OwnLabAIBuild Bayesian models with PyMC using NUTS sampling and LOO/WAIC comparison.
Ritabrata-ChakrabortyGenerate statistical visualizations like scatter plots and heatmaps with Matplotlib and Pandas.
jimmc414Build, fit, and validate Bayesian models with PyMC.
