V-Splines and Bayes Estimate
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Cao, Z. and Bryant, D. and Parry, M. 2018. V-Splines and Bayes Estimate. arXiv.org. 1803.07645: pp. 1-16.
Faculty of Science and Engineering
School of Molecular and Life Sciences (MLS)
Smoothing splines can be thought of as the posterior mean of a Gaussian process regression in a certain limit. By constructing a reproducing kernel Hilbert space with an appropriate inner product, the Bayesian form of the V-spline is derived when the penalty term is a fixed constant instead of a function. An extension to the usual generalized cross-validation formula is utilized to find the optimal V-spline parameters.
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