The -Metropolis (Markov Chain) 4Monte Carlo method is simple and powerful. Since 1953, many extensions of the original LMarkov Chain Monte Carlo method have been proposed, but they are all based on the original Metropolis prescription that only states belonging to the Markov Chain should be sampled. In particular, if trial moves to a potential target state are rejected, that state is not included in the sampling. I will. argue that the efficiency of effectively all Markov Chain MC schemes can be improved by including the rejected states in the sarnpfing procedure. Such an approach requires only a trivial (and cheap) extension of existing prog-rams. I will demonstrate that the approach leads to improved estimates of the ener,v of a system and that it leads to better estimates of free-energy landscapes.

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Cham: Springer
M. Ferrario , G. Ciccotti , K. Binder

Frenkel, D. (2006). Waste-recycling Monte Carlo. In M. Ferrario, G. Ciccotti, & K. Binder (Eds.), Computer simulations in condensed matter : from materials to chemical biology ; Vol. 1 (pp. 127–138).