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PyMC Distributions

Migrate PyMC3 distribution usage to the latest PyMC API.

Updated Mar 19, 2026
Search Tags:#migration#distribution#modeling#bayesian#pymc#pymc3
One-click install
npx skills add https://github.com/benmaier/decision-agent-placeholder --skill pymc-distributions
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: PyMC Distributions
Source: https://github.com/benmaier/decision-agent-placeholder/tree/main/decision-packs/mmm/opencode/skills/distributions
Command: npx skills add https://github.com/benmaier/decision-agent-placeholder --skill pymc-distributions

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps migrate PyMC distribution usage from PyMC3 to the latest PyMC version and fix parameter API mismatches.

Core Features & Use Cases

  • Supports continuous, discrete, multivariate, mixture, and timeseries distributions within PyMC.
  • Assists with API changes, parameter renames, shape handling, and model migration from PyMC3 to newer PyMC versions.
  • Useful for notebook refactors, debugging distribution parameterization, and educational exploration of PyMC distributions.

Quick Start

Convert a PyMC3 distribution call to the latest PyMC API and adjust parameters accordingly.

Frequently Asked Questions about PyMC Distributions

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I migrate PyMC3 distribution parameters to the latest PyMC API?▼

Migrate PyMC3 distributions to the latest PyMC API by fixing parameter mismatches and applying renames like sd to sigma. Ensure compatibility by updating explicit shape handling using dims and coords.

Why does my PyMC3 distribution call fail after updating to the latest PyMC version?▼

PyMC3 distribution calls fail after updating due to API parameter mismatches. Fix these errors by adjusting parameter names, validating observed data, and updating shape handling for the current PyMC API.

Can I refactor continuous and multivariate distributions in a PyMC3 notebook to the new PyMC API?▼

Yes, you can refactor continuous and multivariate distributions in a PyMC3 notebook to the new PyMC API. This process also supports fixing parameter errors across discrete, mixture, and timeseries distributions.

What is the best way to handle shape and coords when migrating PyMC distributions?▼

The best way to handle shape when migrating PyMC distributions is to use explicit dims and coords. This ensures correct dimension alignment and validates observed data within the current PyMC API.

Does this PyMC distribution migration approach support timeseries and mixture distributions?▼

Yes, this PyMC distribution migration approach supports timeseries and mixture distributions. It ensures compatibility with the current PyMC API by fixing parameter mismatches and applying renames across all distribution types.

When do I need to validate observed data during a PyMC model migration?▼

You need to validate observed data during a PyMC model migration when fixing parameter API mismatches. This validation ensures your continuous, discrete, or multivariate distributions are compatible with the updated PyMC API.

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