Zobrazeno 1 - 10
of 466
pro vyhledávání: '"A. Muyskens"'
Publikováno v:
Advances in Statistical Climatology, Meteorology and Oceanography, Vol 10, Pp 143-158 (2024)
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 computationally intensive when the training spatial dataset has a larg
Externí odkaz:
https://doaj.org/article/a5ce8f20a66c4e66986e74a0424d8664
Kernels representing limiting cases of neural network architectures have recently gained popularity. However, the application and performance of these new kernels compared to existing options, such as the Matern kernel, is not well studied. We take a
Externí odkaz:
http://arxiv.org/abs/2410.08311
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 large number of training observations (n) due to the O(n^3) cost of eval
Externí odkaz:
http://arxiv.org/abs/2410.08310
Autor:
Sallaberry, Gregory, Priest, Benjamin W., Armstrong, Robert, Schneider, Michael D., Muyskens, Amanda, Steil, Trevor, Iwabuchi, Keita
Analysis of cosmic shear is an integral part of understanding structure growth across cosmic time, which in-turn provides us with information about the nature of dark energy. Conventional methods generate \emph{shear maps} from which we can infer the
Externí odkaz:
http://arxiv.org/abs/2410.00308
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 computationally intensive when the training spatial dataset has a larg
Externí odkaz:
http://arxiv.org/abs/2409.11577
Autor:
Nnyaba, Ukamaka V., Shemtaga, Hewan M., Collins, David W., Muyskens, Amanda L., Priest, Benjamin W., Billor, Nedret
Analyzing electrocardiography (ECG) data is essential for diagnosing and monitoring various heart diseases. The clinical adoption of automated methods requires accurate confidence measurements, which are largely absent from existing classification me
Externí odkaz:
http://arxiv.org/abs/2409.04642
Autor:
Eleh, Chinedu, Zhang, Yunli, Bidese, Rafael, Priest, Benjamin W., Muyskens, Amanda L., Molinari, Roberto, Billor, Nedret
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 stars has been costly and resource-intensive, involving sophisticated
Externí odkaz:
http://arxiv.org/abs/2407.19297
Gaussian processes (GPs) are Bayesian non-parametric models popular in a variety of applications due to their accuracy and native uncertainty quantification (UQ). Tuning GP hyperparameters is critical to ensure the validity of prediction accuracy and
Externí odkaz:
http://arxiv.org/abs/2209.11280
Autor:
Goumiri, Imène R., Dunton, Alec M., Muyskens, Amanda L., Priest, Benjamin W., Armstrong, Robert E.
Temporal variations of apparent magnitude, called light curves, are observational statistics of interest captured by telescopes over long periods of time. Light curves afford the exploration of Space Domain Awareness (SDA) objectives such as object i
Externí odkaz:
http://arxiv.org/abs/2208.14592
Gaussian processes (GPs) are Bayesian non-parametric models useful in a myriad of applications. Despite their popularity, the cost of GP predictions (quadratic storage and cubic complexity with respect to the number of training points) remains a hurd
Externí odkaz:
http://arxiv.org/abs/2205.10879