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PyMC

Maintainer K-Dense Inc. · Last updated April 1, 2026

PyMC is a Python library for Bayesian modeling and probabilistic programming. Build, fit, validate, and compare Bayesian models using PyMC's modern API (version 5.x+), including hierarchical models, MCMC sampling (NUTS), variational inference, and model comparison (LOO, WAIC).

Claude CodeOpenClawNanoClawAnalysisReproductionpymcmachine-learningpackagemachine learning & deep learning

Original source

K-Dense-AI/claude-scientific-skills

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/pymc

Maintainer: K-Dense Inc.
License: Apache License, Version 2.0
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

PyMC is a Python library for Bayesian modeling and probabilistic programming. Build, fit, validate, and compare Bayesian models using PyMC's modern API (version 5.x+), including hierarchical models, MCMC sampling (NUTS), variational inference, and model comparison (LOO, WAIC).
X =... # Predictors y =... # Outcomes.

Source Doc

Excerpt From SKILL.md

When to Use This Skill

This skill should be used when:

Building Bayesian models (linear/logistic regression, hierarchical models, time series, etc.)
Performing MCMC sampling or variational inference
Conducting prior/posterior predictive checks
Diagnosing sampling issues (divergences, convergence, ESS)
Comparing multiple models using information criteria (LOO, WAIC)
Implementing uncertainty quantification through Bayesian methods
Working with hierarchical/multilevel data structures
Handling missing data or measurement error in a principled way

Standard Bayesian Workflow

Follow this workflow for building and validating Bayesian models:

1. Data Preparation

import pymc as pm
import arviz as az
import numpy as np

Use cases

Building Bayesian models (linear/logistic regression, hierarchical models, time series, etc.).
Performing MCMC sampling or variational inference.
Conducting prior/posterior predictive checks.
Diagnosing sampling issues (divergences, convergence, ESS).

Not for

Do not rely on this catalog entry alone for installation or maintenance details.

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