Analyzing MCMC Output

TL;DR

This paper introduces a comprehensive workflow for analyzing MCMC output, including estimators, stopping rules, and visualization tools, to ensure reliable and trustworthy simulation results.

Contribution

It presents a new workflow for MCMC output analysis that enhances the reliability of simulation experiments through estimators, stopping rules, and visualization.

Findings

Provides methods for assessing MCMC convergence and reliability

Introduces tools for visualizing MCMC output

Offers guidelines for stopping rules in MCMC simulations

Abstract

Markov chain Monte Carlo (MCMC) is a sampling-based method for estimating features of probability distributions. MCMC methods produce a serially correlated, yet representative, sample from the desired distribution. As such it can be difficult to know when the MCMC method is producing reliable results. We introduce some fundamental methods for ensuring a trustworthy simulation experiment. In particular, we present a workflow for output analysis in MCMC providing estimators, approximate sampling distributions, stopping rules, and visualization tools.