Kalman filter [ 1 ] is known to be a robust tool in state estimation for linear or slightly nonlinear systems in diverse disciplines of science and engineering. In the area of structural engineering, Kalman filter has also received enormous attention over the years due to its importance for model updating, response prediction, structural control and health monitoring. The algorithm becomes popular since it provides not only the state estimation but also the associated uncertainty of the estimation. In addition, the algorithm is online so that the state vector is immediately updated once a new data point is obtained. However, the accuracy of Kalman filter depends on the prior knowledge of the process noise and measurement noise parameters, which is difficult to be obtained in practice. In the present study, the Bayesian propabilistic approach [2] is proposed to estimate these noise parameters in the Kalman filter for the case when the input is a zero-mean Gaussian white noise process and limited output measurements are available. The optimal estimates of the noise parameters are chosen by the maximum likelihood criterion. Through the two illustrative examples, the estimated noise parameters are close to the actual values in the sense the actual parameters are located in the region with significant probability density. Therefore, it is concluded that the Bayesian approach is able to provide accurate estimates of the noise parameters, and hence the state estimation, for Kalman filter.