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About 10,000 papers for “Gaussian process”
Agne Tilūnaitė (4497685), Wayne Croft (471450), Noah Russell (4497688) et al. · Figshare · 2017
<p>Three realisations of a GP (red, green, purple) around a time-dependent mean (black line). The blue area delineates the 95% confidence interval. Note the changes in width, which reflect a time-dependent standard deviation.</p>
Cited by 0Open access
Novi Quadrianto (4465081), Kristian Kersting (686383), Zhao Xu (6546) · Figshare · 2010
Cited by 0Open access
Novi Quadrianto, Kristian Kersting, Zhao Xu · Encyclopedia of Machine Learning · 2010
Cited by 11
Mehboob, Irum · Figshare · 2024
This code is used to run GP on the features extracted from ResNet model.
Cited by 0Open access
Quadrianto, N, Kersting, K, Xu, Z et al. · Cambridge University Engineering Department Publications Database
Cited by 0
Charmgil Hong · 2011
Cited by 0
Kersting, Kristian, Xu, Zhao · Sussex Research Online (University of Sussex) · 2010
Cited by 0
Mehboob, Irum · Figshare · 2024
This code is used to run GP on the features extracted from ResNet model.
Cited by 0Open access
Albert Bartók-Pártay · Springer theses · 2010
Cited by 0
André Preumont · Solid mechanics and its applications · 1994
Cited by 0
2013
Cited by 4
Laura Swiler · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2020
Abstract not provided.
Cited by 0Open access
Series in optics and optoelectronics · 2006
Cited by 1
Giuntoli, Andrea, Keten, Sinan, Hansoge, Nitin K. et al. · Figshare · 2021
details of the parameters used for the modeling using Gaussian processes
Cited by 0Open access
Тадевосян Арман Арменович, Tadevosan Arman Armenovic · Research Repository Saint Petersburg State University (Saint Petersburg State University) · 2021
В работе рассмотрен частный случай задачи о случайном назначении. Введено понятие гауссовского процесса назначений. Получен результат, описывающий асимптотическое поведение математического ожидания максимума гауссовского процесса назначений. Показано, что жадная стратегия построения оптимальной перестановки обеспечивает асимптотически оптимальный результат с точки зрения математического ожидания.Также получена двусторонняя асимптотическая оценка модуля непрерывности.
Cited by 0Open access
Xiaofeng Wang, Yuryan Yue, Julian J. Faraway · 2018
Cited by 16
Giuntoli, Andrea, Keten, Sinan, Hansoge, Nitin K. et al. · Figshare · 2021
details of the parameters used for the modeling using Gaussian processes
Cited by 0Open access
Arman Shafieloo, Alex Kim, Eric V. Linder · Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D, Particles, fields, gravitation, and cosmology · 2012
Gaussian processes provide a method for extracting cosmological information from observations without assuming a cosmological model. We carry out cosmography---mapping the time evolution of the cosmic expansion---in a model-independent manner using kinematic variables and a geometric probe of cosmology. Using the state of the art supernova distance data from the Union2.1 compilation, we constrain, without any assumptions about dark energy parametrization or matter density, the Hubble parameter and deceleration parameter as a function of redshift. Extraction of these relations is tested successfully against models with features on various coherence scales, subject to certain statistical cautions.
Cited by 270Open access
Fabio Sigrist · arXiv (Cornell University) · 2020
We introduce a novel way to combine boosting with Gaussian process and mixed effects models. This allows for relaxing, first, the zero or linearity assumption for the prior mean function in Gaussian process and grouped random effects models in a flexible non-parametric way and, second, the independence assumption made in most boosting algorithms. The former is advantageous for prediction accuracy and for avoiding model misspecifications. The latter is important for efficient learning of the fixed effects predictor function and for obtaining probabilistic predictions. Our proposed algorithm is also a novel solution for handling high-cardinality categorical variables in tree-boosting. In addition, we present an extension that scales to large data using a Vecchia approximation for the Gaussian process model relying on novel results for covariance parameter inference. We obtain increased prediction accuracy compared to existing approaches on multiple simulated and real-world data sets.
Cited by 1Open access
Houman Owhadi · Applied Mathematics and Mechanics · 2023
Abstract We present a Gaussian process (GP) approach, called Gaussian process hydrodynamics (GPH) for approximating the solution to the Euler and Navier-Stokes (NS) equations. Similar to smoothed particle hydrodynamics (SPH), GPH is a Lagrangian particle-based approach that involves the tracking of a finite number of particles transported by a flow. However, these particles do not represent mollified particles of matter but carry discrete/partial information about the continuous flow. Closure is achieved by placing a divergence-free GP prior ξ on the velocity field and conditioning it on the vorticity at the particle locations. Known physics (e.g., the Richardson cascade and velocity increment power laws) is incorporated into the GP prior by using physics-informed additive kernels. This is equivalent to expressing ξ as a sum of independent GPs ξ l , which we call modes, acting at different scales (each mode ξ l self-activates to represent the formation of eddies at the corresponding scales). This approach enables a quantitative analysis of the Richardson cascade through the analysis of the activation of these modes, and enables us to analyze coarse-grain turbulence statistically rather than deterministically. Because GPH is formulated by using the vorticity equations, it does not require solving a pressure equation. By enforcing incompressibility and fluid-structure boundary conditions through the selection of a kernel, GPH requires significantly fewer particles than SPH. Because GPH has a natural probabilistic interpretation, the numerical results come with uncertainty estimates, enabling their incorporation into an uncertainty quantification (UQ) pipeline and adding/removing particles (quanta of information) in an adapted manner. The proposed approach is suitable for analysis because it inherits the complexity of state-of-the-art solvers for dense kernel matrices and results in a natural definition of turbulence as information loss. Numerical experiments support the importance of selecting physics-informed kernels and illustrate the major impact of such kernels on the accuracy and stability. Because the proposed approach uses a Bayesian interpretation, it naturally enables data assimilation and predictions and estimations by mixing simulation data and experimental data.
Cited by 5Open access