pymc-bayesian-modeling

Name: pymc-bayesian-modeling
Availability: InStock
Author: rubensliv

Automate Bayesian inference with PyMC for scientific data models.

15|2|Updated Dec 17, 2025

Search Tags:#inference #mcmc #bayesian #pymc #posterior-predictive #hierarchical-models #variational

Ready to run

npx skills add https://github.com/rubensliv/k-dense-ai --skill pymc-bayesian-modeling-rubensliv

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pymc-bayesian-modeling
Source: https://github.com/rubensliv/k-dense-ai/tree/main/scientific-skills/pymc
Command: npx skills add https://github.com/rubensliv/k-dense-ai --skill pymc-bayesian-modeling-rubensliv

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Bayesian inference for scientific data using PyMC, enabling robust parameter estimation and uncertainty quantification.

Core Features & Use Cases

Bayesian modeling with PyMC (MCMC, VI, hierarchical models)
Model diagnostics, posterior predictive checks, and model comparison
Works with data preparation, priors, and named dimensions for clarity

Quick Start

Provide a simple Bayesian regression with PyMC to obtain posterior samples and predictive checks.

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 Bayesian regression model in PyMC with posterior predictive checks?▼

To build a Bayesian regression model in PyMC, you define priors, data containers, and named dimensions, then sample posterior distributions using MCMC and validate them with posterior predictive checks.

Does this PyMC workflow support hierarchical models and variational inference?▼

Yes, this PyMC workflow supports hierarchical models and variational inference, allowing you to fit complex probabilistic models and estimate parameters with credible intervals for scientific data.

What is the best way to compare Bayesian models and run diagnostics in PyMC?▼

The best way to compare Bayesian models in PyMC is by using ArviZ for diagnostics, performing posterior predictive checks, and evaluating model fit to ensure robust uncertainty quantification.

Can I use PyMC for time-series forecasting and uncertainty quantification?▼

Yes, you can use PyMC for time-series forecasting by constructing probabilistic models that yield credible intervals, enabling robust parameter estimation and uncertainty quantification for your data.

Do I need PyMC v5 and ArviZ to run Bayesian inference and model diagnostics?▼

Yes, you need PyMC v5 or higher for Bayesian inference and ArviZ for diagnostics, as the workflow requires these frameworks to execute priors, posterior predictive checks, and model comparison.

Why use named dimensions and data containers when defining Bayesian models in PyMC?▼

Using named dimensions and data containers in PyMC clarifies model structure, ensuring that priors, observed data, and probabilistic relationships are clearly mapped during posterior inference and predictive checks.