Analyzing MCMC Output
Computation
2019-12-10 v2 Methodology
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.
Cite
@article{arxiv.1907.11680,
title = {Analyzing MCMC Output},
author = {Dootika Vats and Nathan Robertson and James M Flegal and Galin L Jones},
journal= {arXiv preprint arXiv:1907.11680},
year = {2019}
}