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In the present paper we describe a stochastic quadrature method that is designed for the evaluation of generalized, complex averages. Motivated by recent advances in sparse sampling techniques, this method is based on a combination of parallel tempering and stationary phase filtering methods. Numerical applications of the resulting ‘‘stationary tempering’’ approach are presented. We also examine inherent structure decomposition from a probabilistic clustering perspective.

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© 2002 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in D. Sabo, J.D. Doll and D.L. Freeman, “Stationary tempering and the complex quadrature problem,” Journal of Chemical Physics, 116(9), 3509 (2002) and may be found at