Interruptible exact sampling in the passive case
  We establish, for various scenarios, whether or not interruptible exact stationary sampling is possible when a finite-state Markov chain can only be viewed passively. In particular, we prove that such sampling is not possible using a single copy of the chain. Such sampling is possible when enough copies of the chain are available, and we provide an algorithm that terminates with probability one. 
Keith Crank (National Science Foundation), James Allen Fill (Johns   Hopkins Univ.)