Bayesian methods for process identification with outliers

The problem of model identification in the presence of outliers has received great attention and a wide variety of outlier identification approaches have been proposed. Yet, there is a great need to seek for more general solutions and a unified framework to solving various practical problems. We propose to formulate the model identification problem under a robust unified framework consisting of consecutive levels of Bayesian inference. The proposed Bayesian inference scheme not only yields maximum a posteriori (MAP) estimates of model parameters, but also provides an automated mechanism for determining hyperparameters of the model parameters´ prior distributions and for investigating the quality of each data point. The effectiveness of the developed robust framework will be demonstrated on the simulated data-sets.