Editing Principle of maximum entropy (section)

===Information entropy as a measure of 'uninformativeness'===
Consider a '''discrete probability distribution''' among <math> m </math> mutually exclusive [[proposition]]s. The most informative distribution would occur when one of the propositions was known to be true. In that case, the information entropy would be equal to zero. The least informative distribution would occur when there is no reason to favor any one of the propositions over the others. In that case, the only reasonable probability distribution would be uniform, and then the information entropy would be equal to its maximum possible value, <math> \log m </math>. The information entropy can therefore be seen as a numerical measure which describes how uninformative a particular probability distribution is, ranging from zero (completely informative) to <math> \log m </math> (completely uninformative).

By choosing to use the distribution with the maximum entropy allowed by our information, the argument goes, we are choosing the most uninformative distribution possible. To choose a distribution with lower entropy would be to assume information we do not possess. Thus the maximum entropy distribution is the only reasonable distribution. The [http://projecteuclid.org/euclid.ba/1340370710 dependence of the solution] on the dominating measure represented by <math> m(x) </math> is however a source of criticisms of the approach since this dominating measure is in fact arbitrary.<ref name=Druihlet2007/>