MLE techniques aims at minimising the Kullback-Leibler divergence between the actual unknown probability distribution and the probability model (Bishop, 2006), defined as Pr(e, f ) log Pr(e, f ) p θ (e, f ) (2) When minimising, Eq.
(2) is simplified tô Pr(e, f ) log(p θ (e, f )) (3) which is approximated by a sufficiently large dataset under the commonly hold assumption that it is independently and identically distributed according to Pr(e, f ) aŝ by perturbing the sample {(e n , f n )} with an active strategy, we are, in fact, modifying the approximation to Eq.