When viewed in terms of information theory, the entropy state function is the amount of information in the system that is needed to fully specify the microstate of the system. Entropy is the measure of the amount of missing information before reception. Often called Shannon entropy, it was originally devised by Claude Shannon in 1948 to study the size of information of a transmitted message. The definition of information entropy is expressed in terms of a discrete set of probabilities p i so that: H ( X ) = − − ∑ ∑ i = 1 n p ( x i ) log ⁡ ⁡ p ( x i ) where the base of the logarithm determines the units (for example, the binary logarithm corresponds to bits).