  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.
