Parameter identification in load models is a critical factor for power system computation, simulation, prediction, as well as stability and reliability analysis. Conventional point estimation based composite load modeling approaches suffer from disturbances and noises, and provide limited information of the systemdynamics. In this work, a statistics (Bayesian Estimation) based distribution estimation approach is proposed for both static (ZIP) and dynamic (induction motor) load models. When dealing with multiple parameters, Gibbs sampling method is employed. The proposed method samples all parameters in each iteration and updates one parameter while others remain fixed. The proposed method provides a distribution estimation for load model coefficients and is robust for measuring errors. The proposed parameter identification approach is generic and can be applied to both transmission and distribution networks. Simulations using a 33-feeder system illustrate the efficiency and robustness of the proposal.