Markov chain Monte Carlo (MCMC) methods make possible the use of flexi
ble Bayesian models that would otherwise be computationally infeasible
. In recent years, a great variety of such applications have been desc
ribed in the literature. Applied statisticians who are new to these me
thods may have several questions and concerns, however: How much effor
t and expertise are needed to design and use a Markov chain sampler? H
ow much confidence can one have in the answers that MCMC produces? How
does the use of MCMC affect the rest of the model-building process? A
t the Joint Statistical Meetings in August, 1996, a panel of experienc
ed MCMC users discussed these and other issues, as well as various ''t
ricks of the trade.'' This article is an edited recreation of that dis
cussion. Its purpose is to offer advice and guidance to novice users o
f MCMC-and to not-so-novice users as well. Topics include building con
fidence in simulation results, methods for speeding and assessing conv
ergence, estimating standard errors, identification of models for whic
h good MCMC algorithms exist, and the current state of software develo
pment.