Robust full Bayesian methods for neural networks
In this paper, we propose a full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. We then propose a reversible jump Markov chain Monte Carlo (...
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| Format: | Conference item |
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Neural information processing systems foundation
2000
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