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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.



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