A helpful way to think about burn-in is that you probably don't care about bias as such but rather error which is a combination of bias and variance. Burn-in re

A helpful way to think about burn-in is that you probably don't care about bias as such but rather error which is a combination of bias and variance. Burn-in reduces bias at the cost of variance (since you have fewer points in your final estimates). My intuition is you want to be somewhere in the middle of the tradeoff. You want to tolerate some bias -- use burn-in to get partway but only partway to the stationary distribution. Here's a paper that advances this way of thinking: http://web.stanford.edu/~ngoodman/papers/LiederGriffithsGoodman2013NIPS.pdf http://web.stanford.edu/~ngoodman/papers/LiederGriffithsGood...