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.