@inproceedings{2abb726a92c44800a65b31b1e2f20157,
title = "An approximate maximum a posteriori method with gaussian process priors",
abstract = "The maximum a posteriori method is generalised for infinite dimensional problems and it is shown that in this case the problem can be reduced to a nonlinear variational problem. This is not a trivial generalisation as the probability density used for the finite dimensional case does not exist. It is shown how the logarithmic gradient can be used to characterise stationary points for the Gaussian process prior case. A nonconforming finite element method is suggested using sparse grids to solve the resulting variational problem.",
keywords = "Machine learning, Maximum a posteriori method",
author = "Markus Hegland",
year = "2006",
doi = "10.1142/9789812772466_0020",
language = "English",
isbn = "9812703918",
series = "Contributions to Probability and Statistics: Applications and Challenges - Proceedings of the International Statistics Workshop",
publisher = "World Scientific Publishing Co. Pte Ltd",
pages = "261--275",
booktitle = "Contributions to Probability and Statistics",
address = "Singapore",
note = "International Statistics Workshop on Contributions to Probability and Statistics: Applications and Challenges ; Conference date: 04-04-2005 Through 05-04-2005",
}