  We introduce bounds on the finite-time performance of Markov chain Monte
Carlo algorithms in approaching the global solution of stochastic optimization
problems over continuous domains. A comparison with other state-of-the-art
methods having finite-time guarantees for solving stochastic programming
problems is included.
