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<p>This essay critically examines well-known arguments against Bayesian Information Criterion (BIC). These arguments claim that I) BIC violates axioms of probability calculus in the case of nested families of statistical hypotheses (‘models’). II) BIC cannot explain why scientists sometimes prefer models with fewer adjustable parameters. And III) BIC’s verdict on a model is sensitive to arbitrary choices of priors over members of models or their parameterization. We argue that although these criticisms contain valuable cautionary advice against the improper use of BIC, they have no force against its general and proper use.</p>

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In defense of Bayesian Information Criterion

  • 期刊论文

  • 发表日期：

    2025年7月11日

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  • Alireza Fatollahi,
  • Kasra Alishahi

摘要

This essay critically examines well-known arguments against Bayesian Information Criterion (BIC). These arguments claim that I) BIC violates axioms of probability calculus in the case of nested families of statistical hypotheses (‘models’). II) BIC cannot explain why scientists sometimes prefer models with fewer adjustable parameters. And III) BIC’s verdict on a model is sensitive to arbitrary choices of priors over members of models or their parameterization. We argue that although these criticisms contain valuable cautionary advice against the improper use of BIC, they have no force against its general and proper use.