A practical Bayesian framework for backpropagation networks
Neural Computation
Issues in Bayesian analysis of neural network models
Neural Computation
Bayesian radial basis functions of variable dimension
Neural Computation
An application of reversible-jump MCMC to multivariate spherical Gaussian mixtures
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Bayesian approach for neural networks—review and case studies
Neural Networks
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Model selection and model averaging for neural networks
Model selection and model averaging for neural networks
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We use neural networks (NN) as a tool for a nonlinear autoregression to predict the second moment of the conditional density of return series. The NN models are compared to the popular econometric GARCH(1,1) model. We estimate the models in a Bayesian framework using Markov chain Monte Carlo posterior simulations. The interlinked aspects of the proposed Bayesian methodology are identification of NN hidden units and treatment of NN complexity based on model evidence. The empirical study includes the application of the designed strategy to market data, where we found a strong support for a nonlinear multilayer perceptron model with two hidden units.