While BIC is a powerful tool, it has limitations. It assumes that the model's parameters are estimated using maximum likelihood estimation. Additionally, BIC might not perform well with small sample sizes, as the penalty term (\( \ln(n) \)) might be too harsh. In such cases, alternative criteria like AIC or cross-validation might be more appropriate.
