Multivariate Interpolation of Wind Field Based on Gaussian Process Regression
نویسندگان
چکیده
منابع مشابه
Gaussian Process Regression for Multivariate Spectroscopic Calibration
Traditionally multivariate calibration models have been developed using regression based techniques including principal component regression and partial least squares and their non-linear counterparts. This paper proposes the application of Gaussian process regression as an alternative method for the development of a calibration model. By formulating the regression problem in a probabilistic fr...
متن کاملEnergy-Driven Image Interpolation Using Gaussian Process Regression
Image interpolation, as a method of obtaining a high-resolution image from the corresponding low-resolution image, is a classical problem in image processing. In this paper, we propose a novel energy-driven interpolation algorithm employing Gaussian process regression. In our algorithm, each interpolated pixel is predicted by a combination of two information sources: first is a statistical mode...
متن کاملRBF interpolation and Gaussian process regression through an RKHS formulation
Radial Basis Function (RBF) interpolation is a common approach to scattered data interpolation. Gaussian Process regression is also a common approach to estimating statistical data. Both techniques play a central role, for example, in statistical or machine learning, and recently they have been increasingly applied in other fields such as computer graphics. In this survey we describe the formul...
متن کاملHierarchical Gaussian Process Regression
We address an approximation method for Gaussian process (GP) regression, where we approximate covariance by a block matrix such that diagonal blocks are calculated exactly while off-diagonal blocks are approximated. Partitioning input data points, we present a two-layer hierarchical model for GP regression, where prototypes of clusters in the upper layer are involved for coarse modeling by a GP...
متن کاملLatent Gaussian Process Regression
We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary processes using stationary GP priors. The approach is built on extending the input space of a regression problem with a latent variable that is used to modulate the covariance function over the input space. We show how our approach can be used to model non-stationary process...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Atmosphere
سال: 2018
ISSN: 2073-4433
DOI: 10.3390/atmos9050194