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pymc-bayesian-modeling

Build and validate Bayesian models with PyMC using MCMC and variational inference.

1|Updated Jan 14, 2026
Search Tags:#statistics#modeling#inference#bayesian#pymc#probabilistic programming
One-click install
npx skills add https://github.com/Sologa/codex-pipeline --skill pymc-bayesian-modeling-sologa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pymc-bayesian-modeling
Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/pymc
Command: npx skills add https://github.com/Sologa/codex-pipeline --skill pymc-bayesian-modeling-sologa

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 empowers users to build, fit, and validate complex Bayesian statistical models, enabling robust probabilistic programming and inference for data-driven decision-making.

Core Features & Use Cases

  • Bayesian Model Building: Construct hierarchical models, regression models (linear, logistic, Poisson), and time series models.
  • Advanced Inference: Perform MCMC sampling (NUTS) and variational inference.
  • Model Validation: Conduct prior and posterior predictive checks, diagnostic analysis, and model comparison (LOO, WAIC).
  • Use Case: Analyze experimental data with uncertainty quantification, build predictive models for sales forecasting, or perform causal inference with complex data structures.

Quick Start

Use the pymc-bayesian-modeling skill to build a linear regression model with the provided data.

Frequently Asked Questions about pymc-bayesian-modeling

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

FAQPage Schema
How do I build a hierarchical Bayesian model using PyMC?▼

Perform Bayesian inference using PyMC with MCMC sampling via the NUTS sampler or variational inference. This Skill facilitates both methods to estimate posterior distributions and quantify uncertainty in your data models.

What is the best way to compare Bayesian models in Python?▼

Yes, PyMC supports Bayesian time series modeling and forecasting. This Skill enables building time series models alongside regression models, performing inference, and analyzing predictive uncertainty for data-driven decisions.

Do I need ArviZ to visualize posterior distributions from PyMC?▼

Yes, ArviZ is required for statistical analysis and visualization of PyMC results. This Skill utilizes ArviZ alongside matplotlib to perform diagnostic analysis and visualize posterior distributions and predictive checks.

When should I use variational inference instead of MCMC in Bayesian modeling?▼

Use variational inference for faster approximation on large datasets, while MCMC via NUTS provides exact posterior exploration. This Skill supports both inference methods, allowing you to choose based on your model complexity and scale.

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