Transformations of gaussian process priors

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3635 LNAI, Issue , 2005, Pages 110-123

  Murray Smith, Roderick ^a,b   Pearlmutter, Barak A ^b  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b NATIONAL UNIVERSITY OF IRELAND   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

IDENTIFICATION (CONTROL SYSTEMS); NONLINEAR CONTROL SYSTEMS; OPTICAL TRANSFER FUNCTION; PROGRAM PROCESSORS; SENSOR DATA FUSION;

LINEAR DYNAMIC SYSTEM; POSTERIOR ESTIMATE; SPARSELY SAMPLED IMAGE;

INFORMATION RETRIEVAL SYSTEMS;

EID: 33646015688     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11559887_7     Document Type: Conference Paper

References (16)

1. Hansen, P. C. (1997). Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion. SIAM. SIAM Monographs on Mathematical Modeling and Computation 4.
2. Leith, D. and Leithead, W. (1999). Analytic framework for blended multiple model systems using linear local models. International Journal of Control, 72(7/8):605-619.
3. Murray-Smith, R. and Sbarbaro, D. (2002). Nonlinear adaptive control using non-parametric Gaussian process prior models. In 15th IFAC World Congress on Automatic Control, Barcelona.
4. O'Hagan, A. (1978). On curve fitting and optimal design for regression (with discussion). Journal of the Royal Statistical Society B, 40:1-42.
5. Ramsay, J. O. and Silverman, B. W. (1997). Functional Data Analysis. Springer-Verlag.
6. Rasmussen, C. E. (1996). Evaluation of Gaussian Processes and other Methods for Non-Linear Regression. PhD thesis. Graduate department of Computer Science, University of Toronto.
7. Seeger, M., Williams, C. K. I., and Lawrence, N. D. (2003). Fast forward selection to speed up sparse Gaussian process regression. In Bishop, C. M. and Frey, B. J., editors, Proceedings of the Ninth International Workshop on AI and Statistics.
8. Shi, J., Murray-Smith, R., Titterington, D., and Pearlmutter, B. (2005). Learning with large data sets using filtered gaussian process priors. In Murray-Smith, R. and Shorten, R., editors, Proceedings of the Hamilton Summer School on Switching and Learning in Feedback systems, volume 3355 of Lecture Notes in Computing Science, pages 128-139. Springer-Verlag.
9. Shi, J. Q., Murray-Smith, R., and Titterington, D. M. (2002). Hierarchical Gaussian process mixtures for regression. Technical Report TR-2002-107, University of Glasgow, Scotland, UK.
10. Solak, E., Murray-Smith, R., Leithead, W. E., Leith, D. J., and Rasmussen, C. E. (2003). Derivative observations in Gaussian process models of dynamic systems. In S. Becker, S. T. and Obermayer, K., editors, Advances in Neural Information Processing Systems 15, pages 1033-1040. MIT Press, Cambridge, MA.
11. Tipping, M. and Bishop, C. M. (2002). Bayesian image super-resolution. In Becker, S., Thrun, S., and Obermeyer, K., editors, Neural Information Processing Systems, volume 12, pages 1303-1310.
12. Tresp, V. (2000). A Bayesian committee machine. Neural. Computation, 12:2719-2741.
13. Wahba, G. (1990). Spline models for observation data. In Regional Conference Series in Applied Mathematics, Philadelphia, PA. SIAM.
14. Williams, C. K. I. (1998a). Computation with infinite neural networks. Neural Computation, 10:1203-1216.
15. Williams, C. K. I. (1998b). Prediction with Gaussian processes: From linear regression to linear prediction and beyond. In Jordan, M. I., editor, Learning and Inference in Graphical Models, pages 599-621. Kluwer.
16. Williams, C. K. I. and Seeger, M. (2001). Using the Nyström method to speed up kernel machines. In T. K. Leen, T. G. Diettrich, V. T., editor, Advances in Neural Information Processing Systems 13, MIT Press.