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Found 2 papers, 1 papers with code
Certified and fast computations with shallow covariance kernels
Many techniques for data science and uncertainty quantification demand efficient tools to handle Gaussian random fields, which are defined in terms of their mean functions and covariance operators.
A practical example for the non-linear Bayesian filtering of model parameters
Importantly, the particle filters enable the adaptive updating of the estimate for $g$ as new observations become available.
Computation Numerical Analysis
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