pymc

Constructs and analyzes Bayesian models using PyMC.

3|Updated Apr 17, 2026

Search Tags:#model comparison #mcmc #pymc #bayesian modeling #variational inference

Ready to run

npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill pymc-ramanebrahimi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pymc
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/pymc
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill pymc-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill allows users to build and analyze Bayesian models, which are useful for probabilistic programming and inference, including hierarchical models, MCMC sampling, and model comparison.

Core Features & Use Cases

Bayesian Modeling: Build hierarchical models with PyMC.
MCMC Sampling: Perform Markov Chain Monte Carlo sampling (NUTS, variational inference).
Model Comparison: Compare models using LOO/WAIC and posterior checks.
Use Case: Suppose you have a dataset with multiple predictors and want to understand the relationships between them probabilistically. Use this Skill to build a Bayesian model, perform MCMC sampling, and compare models to draw conclusions.

Quick Start

Use the pymc skill to build a linear regression model with the provided data and perform MCMC sampling to estimate the parameters.

Frequently Asked Questions about pymc

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

FAQPage Schema

How do I build a Bayesian model for a dataset with multiple predictors?▼

To build a Bayesian model for multiple predictors, you can use this Skill to construct a hierarchical model with PyMC, perform MCMC sampling, and compare models to probabilistically understand variable relationships.

What is the best way to perform MCMC sampling for probabilistic inference?▼

The best way to perform MCMC sampling for probabilistic inference is using PyMC's NUTS sampler or variational inference, which this Skill utilizes to estimate model parameters and generate posterior distributions.

How do I compare Bayesian models using LOO or WAIC?▼

You can compare Bayesian models using LOO or WAIC by leveraging this Skill, which applies the ArviZ library to perform model comparison and posterior predictive checks for statistical analysis.

Can I use NumPy and SciPy data arrays for Bayesian modeling in PyMC?▼

Yes, you can use NumPy and SciPy arrays for Bayesian modeling in PyMC, as this Skill requires both NumPy and SciPy alongside ArviZ to handle model specification, sampling, and visualization.

When should I use variational inference instead of MCMC sampling?▼

You should use variational inference instead of MCMC sampling when you need faster approximate Bayesian inference, as this Skill supports both methods using PyMC for probabilistic programming and statistical analysis.

What dependencies do I need to run PyMC for hierarchical models?▼

To run PyMC for hierarchical models, you need the PyMC, ArviZ, NumPy, and SciPy dependencies, which this Skill uses for probabilistic model specification, MCMC sampling, and visualization.