6 papers
Correspondence of NNGP Kernel and the Matern Kernel
Amanda Muyskens, Benjamin W. Priest, Imene R. Goumiri +1
Kernels representing limiting cases of neural network architectures have recently gained popularity. However, the application and performance of these new kernels compared to exist…
Identifiability and Sensitivity Analysis of Kriging Weights for the Matern Kernel
Amanda Muyskens, Benjamin W. Priest, Imene R. Goumiri +1
Gaussian process (GP) models are effective non-linear models for numerous scientific applications. However, computation of their hyperparameters can be difficult when there is a la…
A Robust Approach to Gaussian Processes Implementation
Juliette Mukangango, Amanda Muyskens, Benjamin W. Priest
Gaussian Process (GP) regression is a flexible modeling technique used to predict outputs and to capture uncertainty in the predictions. However, the GP regression process becomes…
Enhancing Electrocardiography Data Classification Confidence: A Robust Gaussian Process Approach (MuyGPs)
Ukamaka V. Nnyaba, Hewan M. Shemtaga, David W. Collins +3
Analyzing electrocardiography (ECG) data is essential for diagnosing and monitoring various heart diseases. The clinical adoption of automated methods requires accurate confidence…
Stellar Blend Image Classification Using Computationally Efficient Gaussian Processes
Chinedu Eleh, Yunli Zhang, Rafael Bidese +4
Stellar blends, where two or more stars appear blended in an image, pose a significant visualization challenge in astronomy. Traditionally, distinguishing these blends from single…
An Analysis of the Johnson-Lindenstrauss Lemma with the Bivariate Gamma Distribution
Jason Bernstein, Alec M. Dunton, Benjamin W. Priest
Probabilistic proofs of the Johnson-Lindenstrauss lemma imply that random projection can reduce the dimension of a data set and approximately preserve pairwise distances. If a dist…