What version of PyMC does this skill support?

This skill is designed for PyMC 5.x and newer, following the modern API practices and integration with ArviZ for inference data handling.

Is this skill suitable for large datasets?

While MCMC can be slow on massive data, the skill includes patterns for Variational Inference (ADVI) which offers a faster approximation for large-scale exploration.

How does this skill help with sampling divergences?

It provides specific patterns to resolve divergences, such as increasing the target_accept parameter and switching to non-centered parameterization for hierarchical models.

Can I compare different models with this skill?

Yes, it includes standard procedures for model comparison using LOO and WAIC, helping you choose the most appropriate model based on predictive accuracy.

Does it include support for prior predictive checks?

Absolutely. The workflow mandates prior predictive checks to ensure your chosen priors generate plausible data before you begin the computationally expensive sampling process.

PyMC Bayesian Modeling

Name: PyMC Bayesian Modeling
Author: x-cmd

byx-cmd

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数据科学与机器学习

Builds, fits, and validates Bayesian statistical models using PyMC and ArviZ for probabilistic programming.

关于

The PyMC Bayesian Modeling skill provides a robust framework for implementing probabilistic programming directly within your AI-assisted development workflow. It guides users through the entire Bayesian lifecycle, from preparing data and defining weakly informative priors to performing MCMC sampling with the NUTS algorithm and conducting rigorous posterior predictive checks. By incorporating best practices like non-centered parameterization for hierarchical models and diagnostic reporting for R-hat and ESS, this skill ensures that statistical inferences are both computationally efficient and mathematically sound.

主要功能

Hierarchical model templates with non-centered parameterization to avoid divergences
Model comparison tools using LOO (Leave-One-Out) and WAIC information criteria
8 GitHub stars
Guided prior and posterior predictive checks for model validation
Standardized workflow for MCMC sampling (NUTS) and Variational Inference (ADVI)
Comprehensive diagnostic scripts for convergence, R-hat, and effective sample size

使用场景

Developing hierarchical linear or logistic regression models for nested data structures
Quantifying uncertainty in predictions for time-series or count-based data
Implementing robust statistical models that handle missing data and measurement error

关于

主要功能

Hierarchical model templates with non-centered parameterization to avoid divergences
Model comparison tools using LOO (Leave-One-Out) and WAIC information criteria
8 GitHub stars
Guided prior and posterior predictive checks for model validation
Standardized workflow for MCMC sampling (NUTS) and Variational Inference (ADVI)
Comprehensive diagnostic scripts for convergence, R-hat, and effective sample size

使用场景

Developing hierarchical linear or logistic regression models for nested data structures
Quantifying uncertainty in predictions for time-series or count-based data
Implementing robust statistical models that handle missing data and measurement error