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mcmc-sampling-stan

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Master Bayesian inference with Stan.

Data & Analytics#diagnostics#statistical modeling#mcmc#stan#rstan#bayesian inference#py stan
AuthorZurybr
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill provides a comprehensive guide to performing Markov Chain Monte Carlo (MCMC) sampling using Stan, enabling users to implement and validate Bayesian statistical models effectively.

Core Features & Use Cases

  • Bayesian Model Implementation: Guides users through specifying models, priors, and parameters in Stan.
  • MCMC Sampling Configuration: Details how to set up iterations, warmup, and control parameters for robust sampling.
  • Diagnostic Checks: Emphasizes critical convergence and sampling diagnostics (R-hat, ESS, divergences) to ensure model validity.
  • Use Case: When fitting a complex hierarchical model to experimental data, this Skill ensures the Bayesian inference process is correctly set up, sampled, and validated using Stan.

Quick Start

Use the mcmc-sampling-stan skill to guide me through fitting a Bayesian model in Stan, focusing on diagnostic checks.

Dependency Matrix

Required Modules

None required

Components

references

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: mcmc-sampling-stan
Download link: https://github.com/Zurybr/lefarma-skills/archive/main.zip#mcmc-sampling-stan

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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