We propose a general purpose variance reduction technique for Markov Chain Monte
Carlo estimators based on the Zero-Variance principle introduced in the physics lit-
erature by Assaraf and Caffarel (1999. The potential of the new idea is illustrated
with some toy examples and a real application to Bayesian inference for credit risk
estimation.