Monte Carlo beyond Map Reduce

I've written a couple off posts here and here about Monte Carlo methods and Map Reduce. Both have considered naive Monte Carlo in the sense that the sampling distribution is fixed and does not depend on any prior evaluations.  However, more sophisticated Monte Carlo methods do change the sampling distribution over time. Again the wikipedia article is pretty good: http://en.wikipedia.org/wiki/Monte_Carlo_integration. In these cases we iterate over the kinds of map reduce evaluations discussed previously, updating the distribution each iteration. The iterative nature of these methods make them more latency sensitive than the typical MapReduce task. If we are performing many iterations each iteration must not incur a significant start-up delay otherwise the total time may be dominated by this delay. Again this is a setting in which Numerate's distributed framework should shine.