著者

山口 健太郎

出版者

京都大学文学部科学哲学科学史研究室

雑誌

科学哲学科学史研究 (ISSN:18839177)

巻号頁・発行日

vol.2, pp.43-59, 2008-01-31

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Within the framework of statistics, the goodness of statistical models is evaluated by criteria for model selection, such as the Akaike and Bayesian information criteria. Each information criteria is based on likelihoodist’s or Bayesian conception. Here, I analyse the inferences used in the derivation of these criteria, and argue that the goodness, evaluated by the Akaike or Bayesian information criteria reflects frequentist’s conception, which is not explained by likelihoodist or Bayesian.