%0 Book Section %1 aston651 %A Williams, C. K. I. %A Rasmussen, C. E. %B Advances in Neural Information Processing Systems 8 %D 1996 %E Touretzky, D. S. %E Mozer, M. C. %E Hasselmo, M. E. %I MIT %K calibration gaussian prediction processes regression s:2013-08-22_calibration %T Gaussian processes for regression %U http://eprints.aston.ac.uk/651/ %X The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.

@incollection{aston651, abstract = {The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.}, added-at = {2013-08-22T17:08:16.000+0200}, author = {Williams, C. K. I. and Rasmussen, C. E.}, biburl = {https://www.bibsonomy.org/bibtex/2fc35b3e3dd49f57510f9e56dd5f1dc35/becker}, booktitle = {Advances in Neural Information Processing Systems 8}, description = {Gaussian processes for regression - Aston University Research Archive}, editor = {Touretzky, D. S. and Mozer, M. C. and Hasselmo, M. E.}, interhash = {379789a24f50b2dce7da3fb66608e430}, intrahash = {fc35b3e3dd49f57510f9e56dd5f1dc35}, keywords = {calibration gaussian prediction processes regression s:2013-08-22_calibration}, month = {June}, note = {Copyright of the Massachusetts Institute of Technology Press (MIT Press)}, publisher = {MIT}, timestamp = {2013-08-22T17:08:16.000+0200}, title = {Gaussian processes for regression}, url = {http://eprints.aston.ac.uk/651/}, year = 1996 }