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Conference Paper

Occam's Razor

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84156

Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Rasmussen, C. (2001). Occam's Razor. Advances in Neural Information Processing Systems 13, 294-300.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E2B0-B
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.