方法证据记录
Bayesian Information Criterion
The Bayesian Information Criterion is an information-theoretic model selection criterion that approximates Bayesian model comparison. Introduced by Gideon Schwarz in 1978, BIC penalizes model complexity more heavily than AIC by using a sample-size-dependent penalty, making it particularly suitable for identifying the true underlying model structure.
源记录
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Bayesian Information Criterion
分类方法记录 · mcdm / model-evaluation
- 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
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