Fit and evaluate Bayesian models with PyMC and ArviZ diagnostics.
npx skills add https://github.com/m0at/claudemd --skill pymc-bayesian-modeling-m0at
Please help me install this Agent Skill. Skill: pymc-bayesian-modeling Source: https://github.com/m0at/claudemd/tree/main/skills/pymc-bayesian-modeling Command: npx skills add https://github.com/m0at/claudemd --skill pymc-bayesian-modeling-m0at
PyMC Bayesian Modeling empowers data scientists to build, fit, validate, and compare probabilistic models with a clean, scalable workflow in Python.
Load your data, define a PyMC model, and run the sampler to obtain posterior inferences.
High-intent search queries and answers about installing and using this skill.
Build hierarchical models in PyMC using non-centered parameterization, then validate convergence with ArviZ diagnostics. This workflow applies NUTS sampling and posterior predictive checks to ensure robust probabilistic inference.
Compare Bayesian models using LOO and WAIC criteria to evaluate predictive accuracy. PyMC modeling workflows integrate these metrics with posterior predictive checks to select the most robust probabilistic representation of your data.
Yes, PyMC supports both NUTS MCMC sampling and variational inference within the same workflow. You can apply either method to fit probabilistic models and use ArviZ for robust convergence diagnostics.
Quantify time-series uncertainty by defining a probabilistic PyMC model and running the sampler to obtain posterior distributions. This yields robust probabilistic inferences and enables full predictive checks.
Yes, ArviZ is required to perform robust diagnostics for convergence and model fit. It standardizes the evaluation of MCMC sampling outputs and posterior predictive checks in the PyMC workflow.
OwnLabAIBuild Bayesian models with PyMC using NUTS sampling and LOO/WAIC comparison.
jg-youGenerate ArviZ diagnostic and substantive figures for Bayesian analyses.
SciMate-AISolve Bayesian modeling tasks with PyMC for probabilistic programming.
robotlearning123Build Bayesian models with PyMC for MCMC sampling and variational inference.
