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pymc

Construct and analyze Bayesian models with PyMC for probabilistic inference.

13|3|Updated Jun 10, 2026
Search Tags:#MCMC#probabilistic programming#Bayesian modeling#hierarchical modeling#PyMC
One-click install
npx skills add https://github.com/tassiovale/claude-code-kit --skill pymc-tassiovale
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pymc
Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/pymc
Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill pymc-tassiovale

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pymc, numpy, pymc3, tensorboard, arviz, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill enables the creation and analysis of Bayesian models, providing a framework for probabilistic programming and inference.

Core Features & Use Cases

  • Hierarchical Modeling: Build models with hierarchical relationships between variables.
  • Probabilistic Programming: Utilize PyMC's modern API for creating complex probabilistic models.
  • Model Comparison: Compare models using LOO and WAIC.
  • Use Case: Suppose you're analyzing customer behavior data, and you want to understand the impact of different factors (e.g., age, location) on customer lifetime value. You can use this Skill to create a hierarchical model and analyze the effects of the factors on the lifetime value.

Quick Start

Use the pymc skill to create a hierarchical model with PyMC for analyzing customer lifetime value data.

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 hierarchical model for analyzing customer lifetime value?▼

You build a Bayesian hierarchical model by defining variable relationships through PyMC's modern API, enabling probabilistic programming to analyze factors like age and location on customer lifetime value.

What is the difference between MCMC sampling and variational inference in Bayesian modeling?▼

MCMC sampling (NUTS) and variational inference are two Bayesian inference methods supported for probabilistic programming. MCMC sampling explores the posterior distribution more thoroughly, while variational inference offers faster approximation for complex probabilistic models.

Can I use ArviZ to compare Bayesian models using LOO and WAIC?▼

Yes, you can compare Bayesian models using LOO and WAIC. This Skill utilizes ArviZ as a dependency to perform model comparison, helping you evaluate and select the best probabilistic model for your data analysis.

Does Bayesian modeling with PyMC require a specific Python environment?▼

Bayesian modeling with PyMC requires Python 3.12+ and PyMC 6.0.1 compatible dependencies. Your environment must include numpy, pymc3, tensorboard, and arviz to support the computational graph backend for efficient sampling.

What's the best way to specify complex probabilistic models for MCMC sampling?▼

The best way to specify complex probabilistic models for MCMC sampling is using PyMC's computational graph backend. This approach streamlines model specification and enables efficient NUTS sampling for your Bayesian inference tasks.

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