pymc-bayesian-modeling

Builds and validates Bayesian models using the PyMC library.

2|Updated Feb 2, 2026

Search Tags:#statistics #modeling #inference #bayesian #pymc #probabilistic programming

Ready to run

npx skills add https://github.com/Weiwei-Mao/hydrology-skills --skill pymc-bayesian-modeling-weiwei-mao

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pymc-bayesian-modeling
Source: https://github.com/Weiwei-Mao/hydrology-skills/tree/main/hydrology-skills/pymc
Command: npx skills add https://github.com/Weiwei-Mao/hydrology-skills --skill pymc-bayesian-modeling-weiwei-mao

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a comprehensive framework for building, fitting, and validating Bayesian statistical models, enabling robust probabilistic programming and inference.

Core Features & Use Cases

Bayesian Modeling: Construct complex hierarchical models, regressions, and time series models.
MCMC & VI: Perform advanced sampling (NUTS) and variational inference.
Diagnostics & Comparison: Validate model fit with extensive diagnostics and compare models using information criteria (LOO, WAIC).
Use Case: Analyze hydrological data with uncertainty quantification, build predictive models for climate change impacts, or perform complex risk assessments where understanding uncertainty is critical.

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 and validate Bayesian models using PyMC?▼

To build and validate Bayesian models using PyMC, you construct hierarchical, regression, or time series models, then fit them using MCMC sampling or variational inference, and validate the fit using prior/posterior predictive checks and diagnostic analysis.

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

The best way to compare probabilistic models in Bayesian inference is by using information criteria like LOO or WAIC, which this approach supports for evaluating and comparing the predictive accuracy of different fitted models.

Can I perform uncertainty quantification for time series analysis with Bayesian modeling?▼

Yes, you can perform uncertainty quantification for time series analysis with Bayesian modeling by constructing time series models and fitting them through MCMC sampling to capture and quantify predictive uncertainty.

When do I need MCMC sampling versus variational inference for hierarchical models?▼

You need MCMC sampling versus variational inference for hierarchical models when determining your inference method: MCMC provides robust sampling for complex posterior distributions, while variational inference offers faster approximation for large-scale probabilistic programming.

Does this Bayesian modeling approach support prior and posterior predictive checks?▼

Yes, this Bayesian modeling approach supports advanced statistical workflows including both prior and posterior predictive checks, enabling you to validate model assumptions and assess the fit of your probabilistic models.