MCMC Sampling with Stan

About

Provides structured guidance for the end-to-end Bayesian inference workflow using Stan and RStan, from setting up C++ toolchains to validating posterior convergence. It streamlines the creation of hierarchical models, offers specific strategies for custom prior implementation via target log-probability statements, and establishes a rigorous validation framework using diagnostics like Rhat, effective sample size, and divergent transitions to ensure reliable statistical results.

Key Features

• Hierarchical Bayesian model specification patterns
• Custom prior implementation using target log-probability
• System dependency and RStan environment configuration
• Advanced MCMC sampling parameter optimization
• 16 GitHub stars
• Comprehensive convergence diagnostic validation

Use Cases

• Tuning MCMC parameters to resolve divergent transitions or treedepth warnings
• Developing multi-level or hierarchical Bayesian models in R
• Troubleshooting RStan installation and C++ compiler issues