… Gaussian process regression is computationally demanding or even intractable. If data can
be processed sequentially, the recursive regression … /or sparse Gaussian process regression …

Two approaches for on-line Gaussian process regression with low computational and
memory demands are proposed. The first approach assumes known hyperparameters and …

… GP regression methods and Bayesian recursive estimation … such parameters by recursively
propagating the analytical … the art sequential Gaussian Process regression on synthetic …

… monitoring scheme based on the recursive Gaussian process (RGP) model is designed in
this paper. Based on the initial data, a Gaussian process model and the corresponding SPE …

… , Gaussian process regression, to estimate model parameters as functions of states, operating
conditions, and lifetime. Computational efficiency is ensured through a recursive approach …

… To address this issue, this paper proposes a recursive GPSSM method with adaptive
capabilities for both operating domains and Gaussian process (GP) hyperparameters. Specifically, …

… RECURSIVE GAUSSIAN PROCESS BASED RSS MODEL This section will introduce a
recursive Gaussian process based RSS model that serves as a key component in our distributed …

… The Gaussian process (GP) model has been applied to the identification of a process model.
… In this work, a method which recursively updates the covariance matrix is proposed. The …

… We propose a Gaussian process based algorithm that is capable of tracking the dynamic …
The shape of the objects is learned online via a Gaussian process. The proposed method can …

… , 17 an approach based on Gaussian Process (GP) regression 18 is proposed in this paper
to … An evolution of GP is applied through the Treed Gaussian Process (TGP) approach, 19, 20 …