  We study a method, Extra Chance Generalized Hybrid Monte Carlo, to avoid
rejections in the Hybrid Monte Carlo method and related algorithms. In the
spirit of delayed rejection, whenever a rejection would occur, extra work is
done to find a fresh proposal that, hopefully, may be accepted. We present
experiments that clearly indicate that the additional work per sample carried
out in the extra chance approach clearly pays in terms of the quality of the
samples generated.
