basic

AIC and BIC from Log-Likelihood Calculator

AIC = 2k − 2·ln L; BIC = k·ln n − 2·ln L.

Compute the Akaike (AIC) and Bayesian (BIC) information criteria for model comparison from a fitted model's log-likelihood ln L, the number of estimated parameters k, and sample size n. Lower values indicate better fit-vs-complexity trade-off; differences ≥ 10 are considered strong evidence in favour of the lower-criterion model.

Daniel Oh By Daniel Oh
Published Last reviewed 1 min read

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How to use this calculator

  1. Fill in the inputs above using the units you already have.
  2. Values update automatically as you type — no submit button needed.
  3. Hover any result row for the underlying formula and intermediate values.

Formula

AIC = 2k − 2·ln L; BIC = k·ln n − 2·ln L.

In depth

Compute the Akaike (AIC) and Bayesian (BIC) information criteria for model comparison from a fitted model's log-likelihood ln L, the number of estimated parameters k, and sample size n. Lower values indicate better fit-vs-complexity trade-off; differences ≥ 10 are considered strong evidence in favour of the lower-criterion model.