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pymc

Build Bayesian models with PyMC and perform MCMC sampling.

Updated Mar 10, 2026
Search Tags:#statistics#modeling#inference#bayesian#pymc#probabilistic programming
One-click install
npx skills add https://github.com/felixboehm/biochem-allergy --skill pymc-felixboehm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pymc
Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/pymc
Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill pymc-felixboehm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pymc, arviz, pandas, numpy, matplotlib, scipy, 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 sophisticated Bayesian statistical models, enabling robust probabilistic programming and uncertainty quantification.

Core Features & Use Cases

  • Bayesian Modeling: Construct hierarchical, linear, logistic, and time-series models.
  • Probabilistic Programming: Define custom models with PyMC's flexible API.
  • Inference: Perform MCMC sampling (NUTS) and variational inference.
  • Model Validation: Conduct prior/posterior predictive checks and diagnostic analysis.
  • Use Case: Analyze complex experimental data by building a hierarchical model to account for group-level variations, quantify uncertainty in parameter estimates, and predict future outcomes with confidence intervals.

Quick Start

Use the pymc skill to build a Bayesian linear regression model for the provided dataset.

Frequently Asked Questions about pymc

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

FAQPage Schema
How do I build a hierarchical Bayesian model for my dataset?▼

You can build a hierarchical Bayesian model using PyMC to define group-level variations and parameter distributions. This approach quantifies uncertainty in estimates and predicts future outcomes with confidence intervals.

What is the best way to perform MCMC sampling and variational inference in Python?▼

Performing MCMC sampling and variational inference is done using the PyMC library to fit probabilistic models. It applies the NUTS sampler for posterior estimation and variational methods for faster approximate results.

Do I need ArviZ and PyMC installed to diagnose model convergence?▼

Yes, diagnosing model convergence requires a Python environment with PyMC and ArviZ installed. ArviZ handles the diagnostic analysis and visualization needed to validate Bayesian inference results effectively.

How do I conduct prior and posterior predictive checks for Bayesian inference?▼

Conduct prior and posterior predictive checks using PyMC to validate model assumptions against observed data. This process simulates data from your distributions to ensure your Bayesian inference is robust and reliable.

Can I use this approach for Bayesian linear regression and time-series modeling?▼

Yes, you can use PyMC for Bayesian linear regression and time-series modeling. The probabilistic programming API allows you to construct and fit these models to quantify uncertainty in your statistical analysis.

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