At the same time, the first has 3.41 (less) while the second 3.66 bits of informational entropy calculated by Equation (2). For example, the message “There is rain in the Sahara today” contains far more semantic information than “There is rain in London today”. It should be highlighted here that we cover exclusively quantitative aspects of the information followed from Equation (2) and do not consider its semantic aspects. This is definitely complicated in all cases because the free energy, as we discuss below, usually destroys or wipes out the information. As a result, there were attempts to relate the information with some negative entropy (negentropy), considered at the same time as the free energy. At the same time, there was a confusion related to the fact that, despite the obvious similarities and even homology of the Shannon and Boltzmann formulas, entropy was traditionally seen as something negative, while the information from the very beginning was considered as something obviously positive. Norbert Wiener began using the Shannon formula in cybernetics that he created, and Umberto Eco even in poetics. MacArthur and Kermit Hutcheson applied Shannon entropy ( H), which they prefer to name information ( I), to assess species diversity in biology. Claude Shannon himself used it in linguistics. However, when they are not equal and redundancy respectively is more than zero, this formula could be used as a measure of information in quite different fields of knowledge. Maximum entropy is calculated by the same Equation (2) when all of the p i probabilities are equal one to another. The form of H will be recognized as that of entropy as defined in certain formulations of statistical mechanics, where p i is the probability of a system being in cell i of its phase space.” At the same time, he distinguished maximum entropy as information of an absolutely unordered state or message created by a purely discrete Markov process, relative entropy as a ratio of H from Equation (2) to maximum entropy and redundancy that equals one minus relative entropy. 50): “Quantities of the form H = −Σ p ilogp i (the constant K merely amounts to a choice of a unit of measure) play a central role in information theory as measures of information, choice and uncertainty. Here, we should note that Claude Shannon did not contrast information and informational entropy. Very soon, however, it became clear that Equation (2) quantitatively describes any information.

Calibre systems george mikhailovsky