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pymc

Build Bayesian models and inference workflows in PyMC.

74|5|Updated Dec 10, 2025
Search Tags:#mcmc#pymc#probabilistic programming#hierarchical models#bayesian modeling#posterior predictive#loo
One-click install
npx skills add https://github.com/dralkh/seerai --skill pymc-dralkh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pymc
Source: https://github.com/dralkh/seerai/tree/main/skills/pymc
Command: npx skills add https://github.com/dralkh/seerai --skill pymc-dralkh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pymc, arviz, numpy, pandas, matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill removes the complexity of building, fitting, and validating Bayesian models by providing a guided PyMC workflow for probabilistic inference, uncertainty quantification, and model comparison.

Core Features & Use Cases

  • Bayesian Modeling: Create linear, logistic, Poisson, hierarchical, and time-series models with weakly informative priors and named dimensions.
  • Inference and Diagnostics: Run MCMC or variational inference, then check R-hat, ESS, divergences, trace behavior, and posterior predictive fit.
  • Model Comparison and Prediction: Compare models with LOO or WAIC and generate predictions for new data or new groups in research and analytics workflows.

Quick Start

Use the pymc skill to fit a Bayesian model on my data, check diagnostics, and generate posterior predictions.

Frequently Asked Questions about pymc

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build and fit hierarchical Bayesian models using PyMC?▼

Build hierarchical Bayesian models in PyMC by defining weakly informative priors and named dimensions, then fit them using MCMC NUTS sampling or ADVI variational inference for probabilistic regression and classification workflows.

How does posterior predictive checking work for Bayesian model validation?▼

Posterior predictive checking validates Bayesian models by simulating new data from the fitted posterior distribution to assess model fit, identify systematic discrepancies, and quantify uncertainty in your analytical predictions.

Can I compare Bayesian models using LOO or WAIC with ArviZ diagnostics?▼

Compare Bayesian models using ArviZ-based LOO or WAIC information criteria, which estimate out-of-sample predictive accuracy to rank competing models and select the best fit for your research data.

What Python environment do I need to run PyMC Bayesian inference workflows?▼

Running PyMC Bayesian inference workflows requires Python 3.12+, PyMC 6-compatible dependencies, and ArviZ for diagnostics, alongside standard data science libraries like numpy, pandas, and matplotlib.

What is the best way to diagnose MCMC convergence issues like divergences?▼

Diagnose MCMC convergence issues by checking R-hat statistics, effective sample size (ESS), and trace plots for divergences using ArviZ, ensuring your Bayesian sampler explored the posterior distribution reliably.

When should I use ADVI variational inference instead of NUTS sampling?▼

Use ADVI variational inference instead of NUTS sampling when you need faster approximate posterior estimation for large-scale Bayesian models, whereas NUTS provides exact MCMC sampling for complex hierarchical structures.

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