Bayesian Methods for Mixtures of Experts

Part of Advances in Neural Information Processing Systems 8 (NIPS 1995)

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Steve Waterhouse, David MacKay, Anthony Robinson


We present a Bayesian framework for inferring the parameters of a mixture of experts model based on ensemble learning by varia(cid:173) tional free energy minimisation. The Bayesian approach avoids the over-fitting and noise level under-estimation problems of traditional maximum likelihood inference. We demonstrate these methods on artificial problems and sunspot time series prediction.