On Bayes Optimal Prediction based on Mixture Model of Multilayer Neural Networks

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Other Title
  • 階層型ニューラルネットワークの混合モデルによるベイズ最適な予測について

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Abstract

In learning of the probabilistic models, it is important to predict accurately for the output of future observation. On prediction of the future observation, it is not necessary to select a particular model from the model class. The objective here is to predict the output of the future observations accurately. On the other hand, a lot of researches of the prediction methods based on Bayes decision theory for the probabilistic models have been reported. These Bayesian methods are efficient to the prediction with accuracy. In this paper, we, at first, show that the prediction using the mixture model of all neural network models in the model class is bayes optimal. However, this mixture model is difficult to calculate strictly for neural network models, since the complex integration on the parameter space is cannot be calculated for the general priors. We, therefore, propose the new prediction method with asymptotic Bayes optimality, based on Laplacian method which calculates the asymptotic posterior predictive distribution and then removes the integration, apply this method to the multilayer neural network models and verify the efficiency of the proposal through the simulation experiments.

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Details 詳細情報について

  • CRID
    1571417127445952768
  • NII Article ID
    110003233111
  • NII Book ID
    AN10091178
  • Text Lang
    ja
  • Data Source
    • CiNii Articles

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