pymc

Build and analyze Bayesian models with PyMC for MCMC and variational inference.

Updated May 17, 2026

Search Tags:#model comparison #MCMC #variational inference #Bayesian modeling #PyMC

Ready to run

npx skills add https://github.com/galeep/plugin-place --skill pymc-galeep

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pymc
Source: https://github.com/galeep/plugin-place/tree/main/plugins/sci-machine-learning/skills/pymc
Command: npx skills add https://github.com/galeep/plugin-place --skill pymc-galeep

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a comprehensive toolkit for building and analyzing Bayesian models using PyMC, a Python library for probabilistic programming and inference.

Core Features & Use Cases

Bayesian Modeling: Build hierarchical models, perform MCMC sampling, and conduct variational inference.
Model Comparison: Compare models using LOO and WAIC for model selection.
Diagnostics: Check convergence, effective sample size, and divergence issues.
Use Case: If you're working with time series data and need to model trends and seasonality, this skill can help you build and validate ARIMA models using PyMC.

Quick Start

Use the pymc skill to fit a linear regression model to your data and extract the posterior distributions for the coefficients.

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 time series data using Python?▼

Build Bayesian models for time series data using PyMC by defining probabilistic relationships, fitting trends and seasonality, and extracting posterior distributions for model validation and inference.

What is the best way to compare Bayesian models for selection?▼

Compare Bayesian models using PyMC by calculating Leave-One-Out (LOO) and Watanabe-Akaike Information Criterion (WAIC) metrics to evaluate predictive accuracy and select the most appropriate statistical model.

How do I diagnose MCMC convergence and divergence issues?▼

Diagnose MCMC convergence and divergence issues by checking effective sample size and convergence metrics using PyMC alongside ArviZ, ensuring reliable posterior distributions for your Bayesian inference.

Can I perform variational inference instead of MCMC sampling?▼

Perform variational inference as an alternative to MCMC sampling in PyMC to estimate posterior distributions faster, providing scalable probabilistic modeling for large datasets and complex models.

Does PyMC work with pandas and numpy for data processing?▼

PyMC integrates directly with pandas and numpy, allowing you to process input arrays and DataFrames to build and analyze hierarchical Bayesian models and extract posterior coefficients efficiently.