lib-pymc

Name: lib-pymc
Availability: InStock
Author: biomaps-infra

Build Bayesian models with PyMC and run MCMC sampling with NUTS.

Updated Mar 13, 2026

Search Tags:#statistics #modeling #inference #mcmc #bayesian #pymc

Ready to run

npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-pymc

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: lib-pymc
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/lib-pymc
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-pymc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill 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 analysis.

Core Features & Use Cases

Bayesian Modeling: Construct hierarchical models, regressions (linear, logistic, Poisson), and time series models.
Inference Methods: Perform MCMC sampling (NUTS) and variational inference (ADVI).
Diagnostics & Validation: Assess model convergence, fit, and reliability using ArviZ tools.
Use Case: Analyze experimental data with uncertainty quantification, build predictive models for scientific research, or perform complex simulations.

Quick Start

Use the lib-pymc skill to build a Bayesian linear regression model for the provided data.

Frequently Asked Questions about lib-pymc

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

FAQPage Schema

How do I build a Bayesian hierarchical model for my experimental data?▼

You can build a Bayesian hierarchical model by defining probabilistic structures and priors using PyMC. This Skill enables constructing hierarchical regressions, time series models, and analyzing experimental data with full uncertainty quantification.

What is the best way to perform MCMC sampling with NUTS for Bayesian inference?▼

The best way to perform MCMC sampling with NUTS is using this Skill's PyMC integration. It facilitates robust probabilistic programming, enabling efficient MCMC sampling and variational inference for complex statistical models.

Can I use Bayesian modeling to quantify uncertainty in predictive models?▼

Yes, Bayesian modeling quantifies uncertainty in predictive models by sampling posterior distributions. This Skill supports building predictive models for scientific research and performing complex simulations to assess data reliability.

How do I assess model convergence and fit after Bayesian inference?▼

You assess model convergence and fit using integrated ArviZ diagnostic tools. This Skill provides comprehensive model validation capabilities, allowing you to evaluate sampling reliability and assess the overall quality of your Bayesian inference.

Does this Skill support variational inference for large scale data analysis?▼

Yes, this Skill supports variational inference through ADVI alongside MCMC sampling. It enables scalable Bayesian inference for data analysis, allowing you to fit complex probabilistic models efficiently when full MCMC is computationally prohibitive.