  The Zipf's law is the major regularity of statistical linguistics that served
as a prototype for rank-frequency relations and scaling laws in natural
sciences. Here we show that the Zipf's law -- together with its applicability
for a single text and its generalizations to high and low frequencies including
hapax legomena -- can be derived from assuming that the words are drawn into
the text with random probabilities. Their apriori density relates, via the
Bayesian statistics, to general features of the mental lexicon of the author
who produced the text.
