How does this skill help with divergent transitions?

It provides specific code patterns for non-centered parameterization and advice on adjusting sampling parameters like target_accept to resolve geometry-related sampling issues.

Can I use standard NumPy functions inside the model?

No, this skill reinforces the use of PyTensor math operations (pm.math) inside the model context to ensure compatibility with the underlying computational graph.

Does this skill work with PyMC 3 or 4?

While many concepts overlap, this skill is specifically optimized for PyMC 5, focusing on current best practices, PyTensor integration, and the InferenceData output format.

What diagnostic metrics does it support?

It includes patterns for checking R-hat (should be 400), and Pareto k values for LOO-CV.

PyMC 5 Fundamentals

Name: PyMC 5 Fundamentals
Author: choxos

bychoxos

0•

数据科学与机器学习

Facilitates the creation, review, and optimization of Bayesian statistical models using the PyMC 5 framework.

This skill provides a comprehensive foundation for building robust Bayesian models in Python using PyMC 5. It equips developers and data scientists with the necessary patterns for model specification, distribution selection, and advanced sampling techniques like NUTS and ADVI. By integrating ArviZ diagnostic workflows, the skill helps identify and resolve common issues such as divergent transitions through non-centered parameterization, ensuring that statistical inferences are both valid and efficient. It is particularly useful for those migrating from Stan or BUGS, or for anyone implementing the Bayesian workflow described in 'Statistical Rethinking'.

主要功能

01Comprehensive syntax patterns for PyMC 5 model blocks and context managers

02Advanced MCMC sampling configuration and troubleshooting for divergences

03Complete ArviZ diagnostic suite for R-hat, ESS, and posterior predictive checks

040 GitHub stars

05Extensive reference for continuous, discrete, and multivariate distributions

06Guidance on non-centered parameterization and PyTensor math operations

使用场景

01Debugging sampling performance and model convergence issues

02Developing hierarchical Bayesian models with complex dependency structures

03Translating research models from Stan, JAGS, or BUGS into modern Python

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/bayesianagent pymc-fundamentals

For use in Claude.ai and ChatGPT

Download Skill