A Noninformative Prior for Neural Networks |
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Authors: | Lee Herbert KH |
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Affiliation: | (1) Department of Applied Math and Statistics, University of California at Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA |
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Abstract: | While many implementations of Bayesian neural networks use large, complex hierarchical priors, in much of modern Bayesian statistics, noninformative (flat) priors are very common. This paper introduces a noninformative prior for feed-forward neural networks, describing several theoretical and practical advantages of this approach. In particular, a simpler prior allows for a simpler Markov chain Monte Carlo algorithm. Details of MCMC implementation are included. |
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Keywords: | Bayesian statistics improper prior Markov chain Monte Carlo |
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