Robust full Bayesian methods for neural networks

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 (...

Полное описание

Библиографические подробности
Главные авторы:	Andrieu, C, de Freitas, J, Doucet, A
Формат:	Conference item
Опубликовано:	Neural information processing systems foundation 2000

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