The calculation of AIC involves two main components: the likelihood of the model and the number of parameters used in the model. The formula for AIC is:
\[ AIC = 2k - 2\ln(L) \]
where \( k \) is the number of parameters and \( L \) is the maximum likelihood of the model. The lower the AIC value, the better the model is considered to be in terms of balancing goodness-of-fit and complexity.
