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Bayesian Data Analysis, Third Edition, 3rd Edition by Donald B. Rubin, Aki Vehtari, David B. Dunson, Hal S. Stern, John B. Carlin, Andrew Gelman

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Chapter 21

Gaussian process models

In Chapter 20, we considered basis function methods such as splines and kernel regressions, which typically require choice of a somewhat arbitrary set of knots. One can prespecify a grid of many knots and then use variable selection and shrinkage to effectively discard the knots that are not needed, but there may nonetheless be some sensitivity to the initial grid. A high-dimensional grid leads to a heavy computational burden, while a low-dimensional grid may not be sufficiently flexible. Another possibility, ...

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