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The Laplace approximation provides a closed-form model selection objective for neural networks (NN).
Bayesian Data Analysis: Straight-line fitting , pages 511–518
Stephen F. Gull · 1989
Earlier work this paper cites.
Bayesian Methods for Adaptive Models
David J. C. Mackay · 1992
Earlier work this paper cites.
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