7 days ago · Each Gaussian Process (GP) yields distance normal distributions from multiple reference points in the object's coordinate system to its surface, thus providing ...

4 days ago · Abstract: Gaussian processes (GPs) are Bayesian nonparametric models for function approx- imation with principled predictive uncertainty estimates.

1 day ago · Gaussian Process Regression (GPR). GPR is a nonparametric Bayesian machine learning method (28) that is increasingly popular within potential energy surface ...

3 days ago · Gaussian mixture model (GMM) (Zhang et al., 2024) is a common type of probability-based process monitoring model, capable of estimating non-Gaussian ...

4 days ago · This study presents a clustering adaptive Gaussian process regression (CAG) method aiming for real-time prediction for nonlinear structural responses in solid ...

3 days ago · Their general idea for training is to progressively add noise to the training samples in a Gaussian diffusion process. This introduces a pseudo-time and ...

2 days ago · A Bayesian framework is used to systematically and comprehensively optimize the original BSVR hyperparameters and the hyperparameters of the Gaussian process, ...

3 days ago · The paper proposes a novel algorithm for training an ANN from an unlabeled data sample of patterns randomly drawn from an underlying probability density ...

4 days ago · This approach enables it to handle large datasets with high dimensionality and enhances the robustness and accuracy of the model [42]. During our experiments, ...

6 days ago · We employ a fast global sensitivity analysis (FGSA) technique to identify these domain-defining vectors, custom-developed for domain determination. Unlike ...