MCDMInformation-theoretic criterion
贝叶斯信息准则 (BIC)
贝叶斯信息准则是一种信息论模型选择准则，它近似于贝叶斯模型比较。由吉迪恩·施瓦茨于1978年提出，BIC通过使用依赖于样本量的惩罚项，比AIC更严厉地惩罚模型复杂度，这使其特别适合识别真实的潜在模型结构。
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来源
- Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464. DOI: 10.1214/aos/1176344136 ↗
- Burnham, K. P., & Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed.). New York: Springer. DOI: 10.2307/3802723 ↗
- Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773-795. DOI: 10.1080/01621459.1995.10476572 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Information Criterion. ScholarGate. https://scholargate.app/zh/model-evaluation/bayesian-information-criterion
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