A model selection process uses BIC (Bayesian Information Criterion) instead of AIC. What is the key difference between BIC and AIC in model selection?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — a is correct because BIC = -2ln(L) + kln(n) while AIC = -2*ln(L) + 2k. Since ln(n) > 2 when n > e^2 ≈ 7.39, BIC imposes a larger penalty per parameter than AIC for most real datasets, tending to select sparser models.
Full explanation below image
Full Explanation
A is correct because BIC = -2ln(L) + kln(n) while AIC = -2*ln(L) + 2k. Since ln(n) > 2 when n > e^2 ≈ 7.39, BIC imposes a larger penalty per parameter than AIC for most real datasets, tending to select sparser models. B is wrong because AIC does not always select a more complex model — the comparison depends on n and k. C is wrong because both are likelihood-based criteria for in-sample fit adjusted for complexity. D is wrong because the formulas differ in the complexity penalty term.