Coordinate Transformation and Polynomial Chaos for the Bayesian Inference of a Gaussian Process with Parametrized Prior Covariance Function [PDF]
, 2015 This paper addresses model dimensionality reduction for Bayesian inference based on prior Gaussian fields with uncertainty in the covariance function hyper-parameters.
Hoteit, Ibrahim +3 more
core +4 more sources
Cross-Covariance Functions for Multivariate Geostatistics
Statistical Science, 2015 Continuously indexed datasets with multiple variables have become ubiquitous in the geophysical, ecological, environmental and climate sciences, and pose substantial analysis challenges to scientists and statisticians.
Genton, Marc G., Kleiber, William
core +5 more sources
Learning a Depth Covariance Function [PDF]
Computer Vision and Pattern Recognition, 2023 We propose learning a depth covariance function with applications to geometric vision tasks. Given RGB images as input, the covariance function can be flexibly used to define priors over depth functions, predictive distributions given observations, and ...
Eric Dexheimer, A. Davison
semanticscholar +1 more source
Which parameterization of the Matérn covariance function? [PDF]
Spatial Statistics, 2023 The Mat\'ern family of covariance functions is currently the most popularly used model in spatial statistics, geostatistics, and machine learning to specify the correlation between two geographical locations based on spatial distance.
Kesen Wang +3 more
semanticscholar +1 more source
Low-Rank Covariance Function Estimation for Multidimensional Functional Data [PDF]
Journal of the American Statistical Association, 2020 Multidimensional function data arise from many fields nowadays. The covariance function plays an important role in the analysis of such increasingly common data.
Jiayi Wang +2 more
semanticscholar +1 more source
FUNCTIONAL SEQUENTIAL TREATMENT ALLOCATION WITH COVARIATES [PDF]
Econometric Theory, 2023 We consider a sequential treatment problem with covariates. Given a realization of the covariate vector, instead of targeting the treatment with highest conditional expectation, the decision-maker targets the treatment which maximizes a general functional of the conditional potential outcome distribution, e.g., a conditional quantile, trimmed mean, or ...
Kock, Anders Bredahl +2 more
openaire +5 more sources
Simultaneous confidence band for stationary covariance function of dense functional data [PDF]
Journal of Multivariate Analysis, 2019 Inference via simultaneous confidence band is studied for stationary covariance function of dense functional data. A two-stage estimation procedure is proposed based on spline approximation, the first stage involving estimation of all the individual ...
Jiangyan Wang +3 more
semanticscholar +1 more source
Functional PCA With Covariate-Dependent Mean and Covariance Structure
Technometrics, 2022 28 pages, 3 ...
Fei Ding +3 more
openaire +2 more sources
Recovering covariance from functional fragments [PDF]
Biometrika, 2018 Biometrika, in ...
Descary, Marie-Hélène +1 more
openaire +2 more sources
Comparing approximate methods for mock catalogues and covariance matrices – I. Correlation function [PDF]
Monthly notices of the Royal Astronomical Society, 2018 This paper is the first in a set that analyses the covariance matrices of clustering statistics obtained from several approximate methods for gravitational structure formation. We focus here on the covariance matrices of anisotropic two-point correlation
Martha Lippich +21 more
semanticscholar +1 more source