This means that we can use some simplified model m to help estimate the true entropy of a sequence of symbols drawn according to probability p. The more accurate m is, the closer the cross-entropy H(p, m) will be to the true entropy H(p). Thus, the difference between H(p, m) and H(p) is a measure of how accurate a model is. Between two models m 1 and m 2 , the more accurate model will be the one with the lower cross-entropy. (The cross-entropy can never be lower than the true entropy, so a model cannot err by underestimating the true entropy.)