  We propose a new method for estimating rare event probabilities when
independent samples are available. It is assumed that the underlying
probability measures satisfy a large deviations principle with a scaling
parameter $\varepsilon$ that we call temperature. We show how by combining
samples at different temperatures, one can construct an estimator with greatly
reduced variance. Although as presented here the method is not as broadly
applicable as other rare event simulation methods, such as splitting or
importance sampling, it does not require any problem-dependent constructions.
