Consider two probability distributions  and . Usually,  represents the data, the observations, or a measured probability distribution. Distribution  represents instead a theory, a model, a description or an approximation of . The Kullback–Leibler divergence  is then interpreted as the average difference of the number of bits required for encoding samples of  using a code optimized for  rather than one optimized for .  Note that the roles of  and  can be reversed in some situations where that is easier to compute, such as with the expectation–maximization algorithm (EM) and evidence lower bound (ELBO) computations.