mcmc-diagnostics

MCMC convergence diagnostics and analysis

509 stars

bya5c-ai

View on GitHub Installation ↓

Best use case

mcmc-diagnostics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

MCMC convergence diagnostics and analysis

Teams using mcmc-diagnostics should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/mcmc-diagnostics/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/mathematics/skills/mcmc-diagnostics/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/mcmc-diagnostics/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How mcmc-diagnostics Compares

Feature / Agent	mcmc-diagnostics	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

MCMC convergence diagnostics and analysis

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# MCMC Diagnostics

## Purpose

Provides MCMC convergence diagnostics and analysis capabilities for validating Bayesian inference results.

## Capabilities

- Rhat (potential scale reduction) computation
- Effective sample size (ESS) calculation
- Trace plot generation
- Autocorrelation analysis
- Divergence detection
- Energy diagnostic (E-BFMI)

## Usage Guidelines

1. **Convergence Check**: Verify Rhat < 1.01 for all parameters
2. **Sample Quality**: Ensure ESS is sufficient for inference
3. **Visual Inspection**: Review trace plots for mixing
4. **Divergences**: Address divergent transitions

## Tools/Libraries

- ArviZ
- CODA
- MCMCpack

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