 As we have seen, Laplace’s classical theory is restricted to finite spaces, one for which there are only finitely many possible outcomes. When the spaces are countably infinite, the spirit of the classical theory may be upheld by appealing to the information-theoretic principle of maximum entropy, a generalization of the principle of indifference championed by Jaynes (1968). Entropy is a measure of the lack of ‘informativeness’ of a probability function. The more concentrated is the function, the less is its entropy; the more diffuse it is, the greater is its entropy. For a discrete assignment of probabilities \(P = (p_1, p_2,\ldots)\), the entropy of \(P\) is defined as: