Minimum variance lower bound estimation with Gaussian Process models

In this paper, we utilize the probability density function of the data to estimate the minimum variance lower bound (MVLB) of a nonlinear system. For this purpose, the Gaussian Process (GP) model has been used. With this model, given a new input and based on past observations, we naturally obtained the variance of the predictive distribution of the future output, which enabled us to estimate MVLB as well as estimation uncertainty. Also, an advantage of the proposed method over others is its ability to estimate MVLB recursively. The application of this method to the real-life dynamic process (experimental four-tank process) indicates that this approach gives very credible estimates of the MVLB.