Cross-entropy and maximum likelihood ($\arg {\min }_{\theta }{\hat{H}}_n(Q_{\theta })=\arg {\max }_{\theta }{\prod }_t{q}_{\theta }(X_t)$)

For a parametric statistical model with probability mass function $q_{\theta }$ and an independent and identically distributed model, minimizing empirical cross-entropy is maximum likelihood estimation. The logarithm is strictly increasing, so maximizing ${\prod }_t{q}_{\theta }(X_t)$ is equivalent to maximizing ${\sum }_t{\log }_2{q}_{\theta }(X_t)$. Negating and dividing by $n$ gives the displayed minimization. Restricting the model family can prevent the optimum from matching the empirical distribution, and a finite optimizing parameter need not exist. This algebra does not by itself establish accuracy on new data.^[ref]