An Inversion-Free Estimating Equations Approach for Gaussian Process Models
Author:
Affiliation:
1. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois
2. IBM Thomas J. Watson Research Center, Yorktown Heights, New York
3. Department of Statistics, University of Chicago, Chicago, Illinois
Publisher
Informa UK Limited
Subject
Statistics, Probability and Uncertainty,Discrete Mathematics and Combinatorics,Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/10618600.2016.1164056
Reference32 articles.
1. A Matrix-free Approach for Solving the Parametric Gaussian Process Maximum Likelihood Problem
2. Parameter estimation in high dimensional Gaussian distributions
3. Asymptotic properties of computationally efficient alternative estimators for a class of multivariate normal models
4. On the Use of Discrete Laplace Operator for Preconditioning Kernel Matrices
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