Minimum Mean Square Error Estimation Under Gaussian Mixture Statistics
  This paper investigates the minimum mean square error (MMSE) estimation of x, given the observation y = Hx+n, when x and n are independent and Gaussian Mixture (GM) distributed. The introduction of GM distributions, represents a generalization of the more familiar and simpler Gaussian signal and Gaussian noise instance. We present the necessary theoretical foundation and derive the MMSE estimator for x in a closed form. Furthermore, we provide upper and lower bounds for its mean square error (MSE). These bounds are validated through Monte Carlo simulations. 
John T. Flam, Saikat Chatterjee, Kimmo Kansanen, Torbjorn Ekman
math.ST stat.TH