Extra Chance Generalized Hybrid Monte Carlo
Provides a practical improvement for Markov chain Monte Carlo methods used in Bayesian inference and computational statistics.
The paper introduces Extra Chance Generalized Hybrid Monte Carlo to reduce rejections in Hybrid Monte Carlo, showing that the extra work per sample improves sample quality.
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.