Bayesian Information Criterion for estimated model
c = criterionBayesian(n,logL,m)
a 1-by-1 matrix of doubles, integer value, the number of variables in the model
a 1-by-1 matrix of doubles, integer value, the log-likelihood of the model vs the observations
a 1-by-1 matrix of doubles, integer value, greater or equal to 1, the number of observations in the model
a 1-by-1 matrix of doubles, the value of the criterion
This function computes Akaike's information criterion, i.e. AIC :
c = log(m) * n − 2 * ln (L)
where L is the likelihood of the model vs the observations.
If the model has a good fit, the criterion is close to zero (and positive).