Build and analyze Bayesian models with PyMC using MCMC sampling and diagnostics.
npx skills add https://github.com/Hung-3008/agusta --skill pymc-hung-3008
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/Hung-3008/agusta/tree/main/.agents/skills/pymc Command: npx skills add https://github.com/Hung-3008/agusta --skill pymc-hung-3008
Bayesian modeling and probabilistic programming made accessible with PyMC, enabling you to construct, fit, diagnose, and compare formal probabilistic models.
Ask PyMC to build and fit a simple Bayesian model with your data and report the posterior summaries.
High-intent search queries and answers about installing and using this skill.
Build hierarchical Bayesian models in PyMC by defining multilevel priors and likelihoods, then fit them using MCMC sampling or variational inference to quantify uncertainty across grouped data and generate posterior summaries.
Perform posterior predictive checks and model comparison in probabilistic programming by analyzing the InferenceData object with ArviZ, which provides comprehensive diagnostics to assess model fit and select the best model.
Yes, PyMC supports time series and GLM-like probabilistic modeling, allowing you to construct formal models for continuous and categorical outcomes while performing principled prior and posterior analysis.
Yes, you need ArviZ and NumPy to run MCMC sampling and variational inference diagnostics, as PyMC relies on these dependencies to produce the InferenceData object required for comprehensive model assessment.
Avoid Bayesian inference when you lack the computational resources for MCMC sampling or when real-time, ultra-low-latency predictions are required, as hierarchical models and robust diagnostics demand significant processing overhead.
tondevrelGenerate static, animated, and interactive visualizations in Python with Matplotlib.
viniruggeriBuild, fit, and compare Bayesian models with PyMC and ArviZ.
felixboehmBuild Bayesian models with PyMC and perform MCMC sampling.
Lord1EgyptConstruct and validate Bayesian hierarchical models with PyMC and ArviZ.
