1. A method for grouping interrelated sensors of a set of sensors into clusters for use in state estimation models, the method comprising: (A) in a computer, training a separate Gaussian Process Regression for each sensor in the set of sensors, wherein in a Gaussian Process Regression for a sensor y, the sensor y is a target sensor and d remaining sensors of the set are input sensors, the training using a training set of signal values from the sensors to determine a noise variance v for the target sensor y and d kernel widths s k , each kernel width s k representing a relevance of a respective sensor k of the d input sensors in predicting a value of the sensor y; (B) performing a dependency analysis on each sensor of the sensor set by using the noise variance v and the kernel widths s k of the sensor to determine whether or not the sensor is correlated to each of the d other sensors; and (C) grouping sensors of the set of sensors into clusters based on the dependency analysis, wherein sensors grouped in each cluster are removed from a sensor index set containing unprocessed sensors used in grouping of sensors into clusters.