We present a statistical interpretation of beamforming to overcome limitations of standard delay-and-sum (DAS) processing. Both the interference and the signal of interest are viewed as random variables and the distribution of the signal of interest is exploited to maximize the a-posteriori distribution of the aperture signals. In this formulation the beamformer output is a maximum-a-posteriori (MAP) estimator of the signal of interest. We provide a closed form expression for the MAP beamformer and estimate the unknown distribution parameters from the available aperture data using an empirical Bayes approach. We propose a simple scheme that iterates between estimation of distribution parameters and computation of the MAP estimator of the signal of interest, leading to an iterative MAP (iMAP) beamformer. This results in a significant improvement of the contrast compared to DAS without severe increase in computational complexity or need for fine-tuning of parameters. By implementing iMAP on both simulated and experimental data, we show that only 13 transmissions are required to obtain contrast comparable to DAS with 75 plane-waves. The proposed method is compared to other interference suppression techniques such as coherence factor and scaled Wiener processing and shows improved contrast and better preserved speckle pattern.