The problem of stochastic system identification utilizing response measurements only is considered in this paper. A negative log-likelihood function utilized to determine the posterior most probable parameters and their associated uncertainties is formulated by incorporating transmissibility matrix concept, random matrix theory and Bayes' theorem. A numerically iterative coupled method involving the optimization of the parameters in groups is proposed so as to reduce the dimension of the numerical optimization problem involved. The initial guess for the parameters to be optimized is also properly estimated through asymptotic analysis. One novel feature of the proposed method is to avoid repeated time-consuming evaluation of the determinant and inverse of the covariance matrix during optimization due to exploring the statistical properties of the trace of Wishart matrix. The proposed method requires no information about the model of the external input. The theory described in this work is illustrated with synthetic data and field data measured from a laboratory model installed with wireless sensors.