MCMC Sampling with Stan

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.

主な機能

ユースケース