David J.C. MacKay Computation and Neural Systems California Institute of...  Home/Search
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Abstract: The Bayesian model comparison framework is reviewed, and the Bayesian Occam's razor is explained. This framework can be applied to feedforward networks, making possible (1) objective comparisons between solutions using alternative network architectures; (2) objective choice of magnitude and type of weight decay terms; (3) quanti ed estimates of the error bars on network parameters and on network output. The framework also generates a measure of the e ective number of parameters... (Update)
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...data. This is analogous to the de nition of the e ective number of parameters by Wahba [24] Hastie and Tibshirani [8] Moody [13] and MacKay [10]. 3 Let f 0 ; Kg be the eigenvalues of mm . Then we obtain P Data eff = K X i=1 i i 2 : The e ective degrees of...
...analytically. Various approximation schemes have been suggested, here we will use the Bayesian Information Criterion (BIC) approximation [12]. This approximates the integral by a Gaussian in the vicinity of the parameters that maximize the integrant (the so called...
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BibTeX entry: (Update)
David J.C. MacKay, "Bayesian Model Comparison and Backprop Nets," pages 839--846 in John E. Moody et al. (Eds.) Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers, San Mateo, CA, 1992. http://citeseer.ist.psu.edu/article/mackay92bayesian.html More
@inproceedings{ mackay92bayesian,
author = "David J. C. MacKay",
title = "Bayesian Model Comparison and Backprop Nets",
booktitle = "Advances in Neural Information Processing Systems",
volume = "4",
publisher = "Morgan Kaufmann Publishers, Inc.",
editor = "John E. Moody and Steve J. Hanson and Richard P. Lippmann",
pages = "839--846",
year = "1992",
url = "citeseer.ist.psu.edu/article/mackay92bayesian.html" }
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