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真のモデルを含まないパラメトリックモデル族に対するベイズ予測の漸近評価

Bibliographic Information

Other Title
  • シン ノ モデル オ フクマナイ パラメトリック モデルゾク ニ タイスル ベイズ ヨソク ノ ゼンキン ヒョウカ
  • Asymptotics of Bayesian prediction for misspecified models
  • 情報理論
  • ジョウホウ リロン

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Abstract

We consider the sequential prediction problem which is the prediction of the next symbol based on the sequential observation of source symbols. The log loss function in this problem is classified into two types, the instantaneous loss and the cumulative loss. The former is the loss function for the prediction of the only next one symbol. The latter is the sum of the instantaneous loss. We consider the Bayesian prediction for this problem. In Bayesian prediction, it is assumed that the true model lies within a parametrized family of distributions. However, it can be considered that it lies without a parametrized family practically(misspecified models), the true model being unknown. We analyze asymptotics of the cumulative loss for Bayesian prediction under this situation.

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Details

  • CRID
    1520572358761008128
  • NII Article ID
    110008800376
  • NII Book ID
    AA1123312X
  • ISSN
    09135685
  • NDL BIB ID
    11200167
  • Web Site
    http://id.ndl.go.jp/bib/11200167
  • Text Lang
    ja
  • NDL Source Classification
    • ZN33(科学技術--電気工学・電気機械工業--電子工学・電気通信)
  • Data Source
    • NDL
    • CiNii Articles

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