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Learning a Depth Covariance Function [PDF]
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 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 +3 more sources
Brain kernel: A new spatial covariance function for fMRI data [PDF]
NeuroImage, 2021 A key problem in functional magnetic resonance imaging (fMRI) is to estimate spatial activity patterns from noisy high-dimensional signals. Spatial smoothing provides one approach to regularizing such estimates. However, standard smoothing methods ignore
Anqi Wu +6 more
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Which parameterization of the MatΓ©rn 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
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Covariance function of vector self-similar processes [PDF]
, 2009 The paper obtains the general form of the cross-covariance function of vector fractional Brownian motions with correlated components having different self-similarity indices.
FrΓ©dΓ©ric Lavancier +2 more
semanticscholar +10 more sources
A Generic Approach to Covariance Function Estimation Using ARMA-Models
Mathematics, 2020 Covariance function modeling is an essential part of stochastic methodology. Many processes in geodetic applications have rather complex, often oscillating covariance functions, where it is difficult to find corresponding analytical functions for ...
Till Schubert +3 more
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Estimation of low-rank covariance function
Stochastic Processes and Their Applications, 2016 We consider the problem of estimating a low rank covariance function $K(t,u)$ of a Gaussian process $S(t), t\in [0,1]$ based on $n$ i.i.d. copies of $S$ observed in a white noise. We suggest a new estimation procedure adapting simultaneously to the low rank structure and the smoothness of the covariance function.
V Koltchinskii
exaly +3 more sources
Introduction to Neutrosophic Stochastic Processes [PDF]
Neutrosophic Sets and Systems, 2023 In this article, the definition of literal neutrosophic stochastic processes is presented for the first time in the form π©π‘ = ππ‘ + ππ‘πΌ ;πΌ 2 = πΌ where both {π(π‘),π‘ β π} and {π(π‘),π‘ β π} are classical real valued stochastic processes.
Mohamed Bisher Zeina, Yasin Karmouta
doaj +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
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Functional PCA With Covariate-Dependent Mean and Covariance Structure
Technometrics, 2022 28 pages, 3 ...
Fei Ding +3 more
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