PyMC Distributions

Manage, debug, and apply PyMC distributions across PyMC3 and latest versions.

178|13|Updated Mar 19, 2026

Search Tags:#data science #data analysis #code migration #probability distributions #PyMC distributions

Ready to run

npx skills add https://github.com/pymc-labs/decision-lab --skill pymc-distributions-pymc-labs

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: PyMC Distributions
Source: https://github.com/pymc-labs/decision-lab/tree/main/decision-packs/mmm/opencode/skills/distributions
Command: npx skills add https://github.com/pymc-labs/decision-lab --skill pymc-distributions-pymc-labs

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill solves issues related to distribution parameter management, code migration between PyMC3 and the latest versions, and the need for a comprehensive reference guide to PyMC distributions.

Core Features & Use Cases

Distribution Expertise: Navigate and utilize the entire suite of continuous, discrete, multivariate, mixture, and timeseries distributions within PyMC.
Code Migration Support: Simplify and facilitate code porting between PyMC3 and newer versions of PyMC.
Troubleshooting: Address common problems like parameter issues, migration challenges, and debugging of distribution configurations.

Quick Start

Import this skill and start analyzing your data by utilizing PyMC's wide range of probability distributions.

Frequently Asked Questions about PyMC Distributions

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

FAQPage Schema

How do I migrate probability distributions code from PyMC3 to the latest PyMC version?▼

Debugging PyMC distributions involves resolving parameter configuration issues and addressing migration challenges across continuous, discrete, and multivariate types. This Skill provides troubleshooting expertise to identify and fix common errors in probability distribution setups without relying on external dependencies.

What is the best way to manage continuous and multivariate distributions in PyMC?▼

PyMC supports a comprehensive suite of continuous, discrete, multivariate, mixture, and timeseries distributions for data analysis. This Skill helps you navigate and apply these probability distributions directly, ensuring code portability and correct parameter management without external dependencies.

Does this PyMC distributions workflow require external data science dependencies?▼

Troubleshooting PyMC distribution parameter issues involves checking configuration settings, validating continuous and discrete distribution inputs, and addressing migration challenges. This Skill offers targeted debugging expertise to resolve these common parameter problems efficiently.

How do I handle timeseries distributions in PyMC for data analysis?▼

Handling timeseries distributions in PyMC for data analysis involves utilizing the framework's specific timeseries distribution functions. This Skill provides reference guidance and expertise to correctly apply and manage these temporal probability distributions within your data science workflows.