An important problem in the implementation of Markov Chain Monte Carlo
algorithms is to determine the convergence time, or the number of iterations
before the chain is close to stationarity. For many Markov chains used in
practice this time is not known. Even in cases where the convergence time is
known to be polynomial, the theoretical bounds are often too crude to be
practical. Thus, practitioners like to carry out some form of statistical
analysis in order to assess convergence. This has led to the development of a
number of methods known as convergence diagnostics which attempt to diagnose
whether the Markov chain is far from stationarity. We study the problem of
testing convergence in the following settings and prove that the problem is
hard in a computational sense: Given a Markov chain that mixes rapidly, it is
hard for Statistical Zero Knowledge (SZK-hard) to distinguish whether starting
from a given state, the chain is close to stationarity by time t or far from
stationarity at time ct for a constant c. We show the problem is in AM
intersect coAM. Second, given a Markov chain that mixes rapidly it is coNP-hard
to distinguish whether it is close to stationarity by time t or far from
stationarity at time ct for a constant c. The problem is in coAM. Finally, it
is PSPACE-complete to distinguish whether the Markov chain is close to
stationarity by time t or far from being mixed at time ct for c at least 1