Occam's Razor (2001)
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by
Carl Edward Rasmussen
,
Zoubin Ghahramani
| Venue: | In Advances in Neural Information Processing Systems 13 |
| Citations: | 16 - 4 self |
BibTeX
@INPROCEEDINGS{Rasmussen01occam'srazor,
author = {Carl Edward Rasmussen and Zoubin Ghahramani},
title = {Occam's Razor},
booktitle = {In Advances in Neural Information Processing Systems 13},
year = {2001},
pages = {294--300},
publisher = {MIT Press}
}
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Abstract
The Bayesian paradigm apparently only sometimes gives rise to Occam's Razor; at other times very large models perform well. We give simple examples of both kinds of behaviour. The two views are reconciled when measuring complexity of functions, rather than of the machinery used to implement them. We analyze the complexity of functions for some linear in the parameter models that are equivalent to Gaussian Processes, and always find Occam's Razor at work.
Citations
| 513 | Bayesian Learning for Neural Networks - Neal - 1996 |
| 417 | Bayesian interpolation - MacKay - 1992 |
| 122 | CA: The Infinite Gaussian Mixture Model - Rasmussen |
| 44 | Ockham’s razor and Bayesian analysis - Jefferys, Berger - 1992 |
