What version of PyMC does this skill support?

This skill is optimized for modern PyMC versions (5.x+), utilizing the latest API features and ArviZ integration for robust statistical analysis.

Can I use this for model comparison?

Yes, the skill provides specific patterns for using ArviZ to compare multiple models using Information Criteria like LOO (Leave-One-Out cross-validation) and WAIC.

How does it handle hierarchical models?

It implements industry best practices for hierarchical modeling, specifically emphasizing non-centered parameterization to avoid sampling divergences in the geometry of the posterior.

Is it compatible with other Python data science libraries?

Yes, it is designed to work seamlessly within the standard Python scientific stack, including NumPy, Pandas, and ArviZ for visualization.

Does it assist with sampling issues like divergences?

Absolutely. It includes detailed diagnostic workflows for identifying and resolving common MCMC issues like divergences, high R-hat values, and low effective sample size (ESS).

PyMC Bayesian Modeling

Name: PyMC Bayesian Modeling
Author: Blurjp

byBlurjp

0•

데이터 과학 및 ML

Builds and validates advanced Bayesian statistical models using PyMC and modern probabilistic programming techniques.

This skill equips Claude with specialized knowledge for implementing comprehensive Bayesian workflows in Python using the PyMC library. It provides expert guidance on constructing complex hierarchical models, performing MCMC sampling with NUTS, and conducting rigorous model diagnostics via ArviZ. Whether you are dealing with uncertainty quantification, missing data, or multilevel structures, this skill ensures best practices like non-centered parameterization and prior/posterior predictive checks are followed to achieve robust, interpretable inference.

주요 기능

01Hierarchical and multilevel model construction with non-centered parameterization

02Prior and posterior predictive checks for rigorous model validation

030 GitHub stars

04Comprehensive model diagnostics including R-hat, ESS, and divergence checking

05Advanced MCMC sampling (NUTS) and variational inference (ADVI) implementation

06Model comparison and selection using LOO and WAIC criteria

사용 사례

01Estimating uncertainty in complex linear and logistic regression models

02Analyzing grouped or nested data using hierarchical Bayesian structures

03Forecasting and time series analysis using autoregressive processes

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add blurjp/imageprepmcp scientific-pymc

For use in Claude.ai and ChatGPT

Download Skill