Build, fit, and compare Bayesian models with PyMC and ArviZ.
npx skills add https://github.com/viniruggeri/applied-dynamical-systems --skill pymc-viniruggeri
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/viniruggeri/applied-dynamical-systems/tree/main/.agents/skills/pymc Command: npx skills add https://github.com/viniruggeri/applied-dynamical-systems --skill pymc-viniruggeri
PyMC provides a Python-based framework for Bayesian modeling and probabilistic programming, enabling practitioners to build probabilistic models, fit them with MCMC or VI, and assess uncertainty and model adequacy. It unifies data preparation, priors, likelihoods, sampling, diagnostics, and posterior predictive checks within a cohesive workflow.
Fit a simple Bayesian linear regression with PyMC to estimate coefficients and assess predictive uncertainty.
High-intent search queries and answers about installing and using this skill.
Build and fit Bayesian hierarchical models by defining priors and likelihoods, then sampling with MCMC NUTS or variational inference to estimate posterior distributions for regression coefficients.
Compare Bayesian models using information criteria like LOO and WAIC, and validate predictive adequacy by running posterior predictive checks to assess how well models replicate observed data.
Yes, ArviZ integrates directly to provide comprehensive MCMC diagnostics and visualizations, enabling you to trace sampling performance, assess convergence, and plot posterior distributions effectively.
Yes, the framework supports fitting time-series models and stochastic differential equations (SDEs) using PyMC, allowing you to model dynamic systems and estimate parameters with Bayesian inference.
Yes, you need PyMC 5.x or higher along with ArviZ, NumPy, Pandas, and Matplotlib to execute the complete workflow of data preparation, Bayesian sampling, diagnostics, and visualization.
Use variational inference (ADVI) for fast approximate Bayesian inference on large datasets when computational speed is critical, and use MCMC NUTS sampling when precise posterior estimation is required.
swaruplabBuild and infer Bayesian models with PyMC using MCMC and variational inference.
jaechang-hitsPerform Bayesian modeling workflows with PyMC and ArviZ.
SologaBuild and validate Bayesian models with PyMC using MCMC and variational inference.
ZurybrGuide MCMC sampling with RStan or PyStan for Bayesian models.
