shown in Figs. 4.5 and 4.6 for the distribution plot and the correlation plot, respectively. We observe from these two plots a remarkable resemblance between the two distributions and a strong correlation between them. 13 This result indicates that any large and random sample of English text, provided it does not include specialized words or acronyms, closely complies with the symbol- distribution statistics. Such compliance is not as surprising as the fact that it is SO good considering the relatively limited size of the sample paragraph. It is noteworthy that frequency analysis was invented as early as the ninth century, by an Arab scientist and linguist, Al-Kindi. 11 This experiment is easy to perform with a home computer. First write or copy and paste a paragraph on any subject, which should include at least N = 1000 alphabetical characters. Then use the find and replace command to substitute letter A with a blank, and SO on until Z. Each time, the computer will indicate how many substitutions were effected, which directly provides the corresponding letter count. The data can then be tabulated at each step and the character counts changed into probabilities by dividing them by N. The whole measurement and tabulating operation should take less than ten minutes. 12 S. Singh, The Code Book: The Science of Secrecy from Ancient Egypt to Quantum Cryptography (New York: Anchor Books, 1999). The reader may trust that this was a single-shot experiment, the plots being realized after having written the sample paragraph, with no attempt to modify it retroactively in view of improving the agreement. Entropy English reference Sample paragraph ETAOINSHRDLCUMWFGYPBVKJX Figure 4.5 Probability distributions of symbol-characters used in English reference (as per a 1982 survey) and as computed from the author's sample paragraph shown in the text. English-symbol reference probability (%) Figure 4.6 Correlation between English reference and author's sample probability distributions. 4.4 Maximum entropy (discrete source) As an int