Issues in Bayesian Analysis of Neural
نویسنده
چکیده
Stemming from work by Buntine and Weigend (1991) and MacKay (1992), there is a growing interest in Bayesian analysis of Neural Network Models. We study computational approaches to the problem, suggesting an eecient Markov chain Monte Carlo scheme for inference and prediction with xed architecture feed forward neural networks. The scheme is then extended to the variable architecture case, providing a procedure for automatic data driven choice of architecture.
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